diff --git a/.github/ISSUE_TEMPLATE/1-bug-report.yml b/.github/ISSUE_TEMPLATE/1-bug-report.yml
index b98e20f2d..9d2b2a4c1 100644
--- a/.github/ISSUE_TEMPLATE/1-bug-report.yml
+++ b/.github/ISSUE_TEMPLATE/1-bug-report.yml
@@ -22,9 +22,9 @@ body:
       label: Minimal Reproducible Example (optional)
       description: Please include a short R code snippet that reproduces the problem, if possible.
       placeholder: 
-        e.g.
-        ```r
-        ab_name("amoxicillin/clavulanic acid", language = "es")
+        e.g.\n\n
+        ```r<br>
+        ab_name("amoxicillin/clavulanic acid", language = "es")\n
         ```
     validations:
       required: false
@@ -42,7 +42,7 @@ body:
       multiple: false
       options:
         - ''
-        - Latest CRAN version (3.0.0)
-        - One of the latest GitHub versions (3.0.0.9xxx)
+        - Latest CRAN version (3.0.1)
+        - One of the latest GitHub versions (3.0.1.9xxx)
     validations:
       required: true
diff --git a/DESCRIPTION b/DESCRIPTION
index 74aee7bf4..152fee550 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: AMR
-Version: 3.0.1.9004
-Date: 2025-12-15
+Version: 3.0.1.9005
+Date: 2025-12-21
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)
   data analysis and to work with microbial and antimicrobial properties by
diff --git a/NAMESPACE b/NAMESPACE
index 25a97f715..0f5029cd6 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -106,6 +106,8 @@ S3method(print,mo_uncertainties)
 S3method(print,pca)
 S3method(print,sir)
 S3method(print,sir_log)
+S3method(print,step_mic_log2)
+S3method(print,step_sir_numeric)
 S3method(quantile,mic)
 S3method(rep,ab)
 S3method(rep,av)
@@ -159,6 +161,12 @@ export(administrable_per_os)
 export(age)
 export(age_groups)
 export(all_antimicrobials)
+export(all_disk)
+export(all_disk_predictors)
+export(all_mic)
+export(all_mic_predictors)
+export(all_sir)
+export(all_sir_predictors)
 export(aminoglycosides)
 export(aminopenicillins)
 export(amr_class)
@@ -352,6 +360,8 @@ export(sir_df)
 export(sir_interpretation_history)
 export(sir_predict)
 export(skewness)
+export(step_mic_log2)
+export(step_sir_numeric)
 export(streptogramins)
 export(sulfonamides)
 export(susceptibility)
@@ -390,6 +400,12 @@ if(getRversion() >= "3.0.0") S3method(pillar::type_sum, av)
 if(getRversion() >= "3.0.0") S3method(pillar::type_sum, mic)
 if(getRversion() >= "3.0.0") S3method(pillar::type_sum, mo)
 if(getRversion() >= "3.0.0") S3method(pillar::type_sum, sir)
+if(getRversion() >= "3.0.0") S3method(recipes::bake, step_mic_log2)
+if(getRversion() >= "3.0.0") S3method(recipes::bake, step_sir_numeric)
+if(getRversion() >= "3.0.0") S3method(recipes::prep, step_mic_log2)
+if(getRversion() >= "3.0.0") S3method(recipes::prep, step_sir_numeric)
+if(getRversion() >= "3.0.0") S3method(recipes::tidy, step_mic_log2)
+if(getRversion() >= "3.0.0") S3method(recipes::tidy, step_sir_numeric)
 if(getRversion() >= "3.0.0") S3method(skimr::get_skimmers, ab)
 if(getRversion() >= "3.0.0") S3method(skimr::get_skimmers, disk)
 if(getRversion() >= "3.0.0") S3method(skimr::get_skimmers, mic)
diff --git a/NEWS.md b/NEWS.md
index c60757907..0823bbb0b 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,10 @@
-# AMR 3.0.1.9004
+# AMR 3.0.1.9005
+
+### New
+* Integration with the **tidymodels** framework to allow seamless use of SIR, MIC and disk data in modelling pipelines via `recipes`
+  - `step_mic_log2()` to transform `<mic>` columns with log2, and `step_sir_numeric()` to convert `<sir>` columns to numeric
+  - New `tidyselect` helpers: `all_sir()`, `all_sir_predictors()`, `all_mic()`, `all_mic_predictors()`, `all_disk()`, `all_disk_predictors()`
+* Data set `esbl_isolates` to practise with AMR modelling
 
 ### Changed
 * Fixed a bug in `antibiogram()` for when no antimicrobials are set
@@ -48,7 +54,7 @@ This is a bugfix release following the release of v3.0.0 in June 2025.
 This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the [University of Prince Edward Island's Atlantic Veterinary College](https://www.upei.ca/avc), Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.
 
 ### Breaking
-* Dataset `antibiotics` has been renamed to `antimicrobials` as the data set contains more than just antibiotics. Using `antibiotics` will still work, but now returns a warning.
+* Data set `antibiotics` has been renamed to `antimicrobials` as the data set contains more than just antibiotics. Using `antibiotics` will still work, but now returns a warning.
 * Removed all functions and references that used the deprecated `rsi` class, which were all replaced with their `sir` equivalents over two years ago.
 * Functions `resistance_predict()` and `sir_predict()` are now deprecated and will be removed in a future version. Use the `tidymodels` framework instead, for which we [wrote a basic introduction](https://amr-for-r.org/articles/AMR_with_tidymodels.html).
 
@@ -60,7 +66,7 @@ This package now supports not only tools for AMR data analysis in clinical setti
   * `ab_atc()` now supports ATC codes of veterinary antimicrobials (that all start with "Q")
   * `ab_url()` now supports retrieving the WHOCC url of their ATCvet pages
 * **Support for WISCA antibiograms**  
-  * The `antibiogram()` function now supports creating true Weighted-Incidence Syndromic Combination Antibiograms (WISCA), a powerful Bayesian method for estimating regimen coverage probabilities using pathogen incidence and antimicrobial susceptibility data. WISCA offers improved precision for syndrome-specific treatment, even in datasets with sparse data. A dedicated `wisca()` function is also available for easy usage.
+  * The `antibiogram()` function now supports creating true Weighted-Incidence Syndromic Combination Antibiograms (WISCA), a powerful Bayesian method for estimating regimen coverage probabilities using pathogen incidence and antimicrobial susceptibility data. WISCA offers improved precision for syndrome-specific treatment, even in data sets with sparse data. A dedicated `wisca()` function is also available for easy usage.
 * **More global coverage of languages**
   * Added full support for 8 new languages: Arabic, Bengali, Hindi, Indonesian, Korean, Swahili, Urdu, and Vietnamese. The `AMR` package is now available in 28 languages.
 * **Major update to fungal taxonomy and tools for mycologists**
diff --git a/R/aa_helper_functions.R b/R/aa_helper_functions.R
index ad96299c2..0afd91f82 100644
--- a/R/aa_helper_functions.R
+++ b/R/aa_helper_functions.R
@@ -966,8 +966,13 @@ get_current_data <- function(arg_name, call) {
       # an element `.data` will be in the environment when using dplyr::select()
       return(env$`.data`)
     } else if (valid_df(env$training)) {
-      # an element `training` will be in the environment when using some tidymodels functions such as `prep()`
-      return(env$training)
+      if (!is.null(env$x) && valid_df(env$x$template)) {
+        # an element `x$template` will be in the environment when using some tidymodels functions such as `prep()`
+        return(env$x$template)
+      } else {
+        # this is a fallback for some tidymodels functions such as `prep()`
+        return(env$training)
+      }
     } else if (valid_df(env$data)) {
       # an element `data` will be in the environment when using older dplyr versions, or some tidymodels functions such as `fit()`
       return(env$data)
diff --git a/R/antibiogram.R b/R/antibiogram.R
index 17c2f9135..f0a8a2163 100755
--- a/R/antibiogram.R
+++ b/R/antibiogram.R
@@ -163,7 +163,7 @@
 #'          antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
 #'    ```
 #'
-#'    WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre datasets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).
+#'    WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre data sets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).
 #'
 #' ### Grouped tibbles
 #'
diff --git a/R/atc_online.R b/R/atc_online.R
index 6cfed0b82..7a24fa543 100755
--- a/R/atc_online.R
+++ b/R/atc_online.R
@@ -99,7 +99,8 @@ atc_online_property <- function(atc_code,
   read_html <- import_fn("read_html", "xml2")
 
   if (!all(atc_code %in% unlist(AMR::antimicrobials$atc))) {
-    atc_code <- as.character(ab_atc(atc_code, only_first = TRUE))
+    missing <- atc_code %unlike% "[A-Z][0-9][0-9][A-Z][A-Z][0-9][0-9]"
+    atc_code[missing] <- as.character(ab_atc(atc_code[missing], only_first = TRUE))
   }
 
   if (!has_internet()) {
diff --git a/R/data.R b/R/data.R
index cb0be376a..841e2fece 100755
--- a/R/data.R
+++ b/R/data.R
@@ -282,7 +282,7 @@
 
 #' Data Set with Clinical Breakpoints for SIR Interpretation
 #'
-#' @description Data set containing clinical breakpoints to interpret MIC and disk diffusion to SIR values, according to international guidelines. This dataset contain breakpoints for humans, `r length(unique(clinical_breakpoints$host[!clinical_breakpoints$host %in% clinical_breakpoints$type]))` different animal groups, and ECOFFs.
+#' @description Data set containing clinical breakpoints to interpret MIC and disk diffusion to SIR values, according to international guidelines. This data set contains breakpoints for humans, `r length(unique(clinical_breakpoints$host[!clinical_breakpoints$host %in% clinical_breakpoints$type]))` different animal groups, and ECOFFs.
 #'
 #' These breakpoints are currently implemented:
 #' - For **clinical microbiology**: EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST" & type == "human")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST" & type == "human")$guideline)))` and CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "human")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "human")$guideline)))`;
@@ -362,14 +362,14 @@
 #' dosage
 "dosage"
 
-# TODO #' Data Set with `r format(nrow(esbl_isolates), big.mark = " ")` ESBL Isolates
-# TODO #'
-# TODO #' A data set containing `r format(nrow(esbl_isolates), big.mark = " ")` microbial isolates with MIC values of common antibiotics and a binary `esbl` column for extended-spectrum beta-lactamase (ESBL) production. This data set contains randomised fictitious data but reflects reality and can be used to practise AMR-related machine learning, e.g., classification modelling with [tidymodels](https://amr-for-r.org/articles/AMR_with_tidymodels.html).
-# TODO #' @format A [tibble][tibble::tibble] with `r format(nrow(esbl_isolates), big.mark = " ")` observations and `r ncol(esbl_isolates)` variables:
-# TODO #' - `esbl`\cr Logical indicator if the isolate is ESBL-producing
-# TODO #' - `genus`\cr Genus of the microorganism
-# TODO #' - `AMC:COL`\cr MIC values for 17 antimicrobial agents, transformed to class [`mic`] (see [as.mic()])
-# TODO #' @details See our [tidymodels integration][amr-tidymodels] for an example using this data set.
-# TODO #' @examples
-# TODO #' esbl_isolates
-# TODO "esbl_isolates"
+#' Data Set with `r format(nrow(esbl_isolates), big.mark = " ")` ESBL Isolates
+#'
+#' A data set containing `r format(nrow(esbl_isolates), big.mark = " ")` microbial isolates with MIC values of common antibiotics and a binary `esbl` column for extended-spectrum beta-lactamase (ESBL) production. This data set contains randomised fictitious data but reflects reality and can be used to practise AMR-related machine learning, e.g., classification modelling with [tidymodels](https://amr-for-r.org/articles/AMR_with_tidymodels.html).
+#' @format A [tibble][tibble::tibble] with `r format(nrow(esbl_isolates), big.mark = " ")` observations and `r ncol(esbl_isolates)` variables:
+#' - `esbl`\cr Logical indicator if the isolate is ESBL-producing
+#' - `genus`\cr Genus of the microorganism
+#' - `AMC:COL`\cr MIC values for 17 antimicrobial agents, transformed to class [`mic`] (see [as.mic()])
+#' @details See our [tidymodels integration][amr-tidymodels] for an example using this data set.
+#' @examples
+#' esbl_isolates
+"esbl_isolates"
diff --git a/R/tidymodels.R.no_include b/R/tidymodels.R
similarity index 85%
rename from R/tidymodels.R.no_include
rename to R/tidymodels.R
index 100d5078c..7b2500dbf 100644
--- a/R/tidymodels.R.no_include
+++ b/R/tidymodels.R
@@ -1,20 +1,21 @@
 #' AMR Extensions for Tidymodels
 #'
-#' This family of functions allows using AMR-specific data types such as `<mic>` and `<sir>` inside `tidymodels` pipelines.
+#' This family of functions allows using AMR-specific data types such as `<sir>` and `<mic>` inside `tidymodels` pipelines.
 #' @inheritParams recipes::step_center
 #' @details
 #' You can read more in our online [AMR with tidymodels introduction](https://amr-for-r.org/articles/AMR_with_tidymodels.html).
 #'
 #' Tidyselect helpers include:
-#' - [all_mic()] and [all_mic_predictors()] to select `<mic>` columns
-#' - [all_sir()] and [all_sir_predictors()] to select `<sir>` columns
+#' - [all_sir()] and [all_sir_predictors()] to select [`<sir>`][as.sir()] columns
+#' - [all_mic()] and [all_mic_predictors()] to select [`<mic>`][as.mic()] columns
+#' - [all_disk()] and [all_disk_predictors()] to select [`<disk>`][as.disk()] columns
 #'
 #' Pre-processing pipeline steps include:
-#' - [step_mic_log2()] to convert MIC columns to numeric (via `as.numeric()`) and apply a log2 transform, to be used with [all_mic_predictors()]
 #' - [step_sir_numeric()] to convert SIR columns to numeric (via `as.numeric()`), to be used with [all_sir_predictors()]: `"S"` = 1, `"I"`/`"SDD"` = 2, `"R"` = 3. All other values are rendered `NA`. Keep this in mind for further processing, especially if the model does not allow for `NA` values.
+#' - [step_mic_log2()] to convert MIC columns to numeric (via `as.numeric()`) and apply a log2 transform, to be used with [all_mic_predictors()]
 #'
 #' These steps integrate with `recipes::recipe()` and work like standard preprocessing steps. They are useful for preparing data for modelling, especially with classification models.
-#' @seealso [recipes::recipe()], [as.mic()], [as.sir()]
+#' @seealso [recipes::recipe()], [as.sir()], [as.mic()], [as.disk()]
 #' @name amr-tidymodels
 #' @keywords internal
 #' @export
@@ -66,35 +67,55 @@
 #'     bind_cols(out_testing)
 #'
 #'   # Evaluate predictions using standard classification metrics
-#'   our_metrics <- metric_set(accuracy, kap, ppv, npv)
+#'   our_metrics <- metric_set(accuracy,
+#'                             recall,
+#'                             precision,
+#'                             sensitivity,
+#'                             specificity,
+#'                             ppv,
+#'                             npv)
 #'   metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
 #'
 #'   # Show performance
 #'   metrics
 #' }
-all_mic <- function() {
-  x <- tidymodels_amr_select(levels(NA_mic_))
-  names(x)
-}
-
-#' @rdname amr-tidymodels
-#' @export
-all_mic_predictors <- function() {
-  x <- tidymodels_amr_select(levels(NA_mic_))
-  intersect(x, recipes::has_role("predictor"))
-}
-
-#' @rdname amr-tidymodels
-#' @export
 all_sir <- function() {
-  x <- tidymodels_amr_select(levels(NA_sir_))
+  x <- tidymodels_amr_select(class = "sir")
   names(x)
 }
 
 #' @rdname amr-tidymodels
 #' @export
 all_sir_predictors <- function() {
-  x <- tidymodels_amr_select(levels(NA_sir_))
+  x <- tidymodels_amr_select(class = "sir")
+  intersect(x, recipes::has_role("predictor"))
+}
+
+#' @rdname amr-tidymodels
+#' @export
+all_mic <- function() {
+  x <- tidymodels_amr_select(class = "mic")
+  names(x)
+}
+
+#' @rdname amr-tidymodels
+#' @export
+all_mic_predictors <- function() {
+  x <- tidymodels_amr_select(class = "mic")
+  intersect(x, recipes::has_role("predictor"))
+}
+
+#' @rdname amr-tidymodels
+#' @export
+all_disk <- function() {
+  x <- tidymodels_amr_select(class = "disk")
+  names(x)
+}
+
+#' @rdname amr-tidymodels
+#' @export
+all_disk_predictors <- function() {
+  x <- tidymodels_amr_select(class = "disk")
   intersect(x, recipes::has_role("predictor"))
 }
 
@@ -160,7 +181,6 @@ bake.step_mic_log2 <- function(object, new_data, ...) {
 print.step_mic_log2 <- function(x, width = max(20, options()$width - 35), ...) {
   title <- "Log2 transformation of MIC columns"
   recipes::print_step(x$columns, x$terms, x$trained, title, width)
-  invisible(x)
 }
 
 #' @rawNamespace if(getRversion() >= "3.0.0") S3method(recipes::tidy, step_mic_log2)
@@ -236,7 +256,6 @@ bake.step_sir_numeric <- function(object, new_data, ...) {
 print.step_sir_numeric <- function(x, width = max(20, options()$width - 35), ...) {
   title <- "Numeric transformation of SIR columns"
   recipes::print_step(x$columns, x$terms, x$trained, title, width)
-  invisible(x)
 }
 
 #' @rawNamespace if(getRversion() >= "3.0.0") S3method(recipes::tidy, step_sir_numeric)
@@ -250,13 +269,13 @@ tidy.step_sir_numeric <- function(x, ...) {
   res
 }
 
-tidymodels_amr_select <- function(check_vector) {
+tidymodels_amr_select <- function(class) {
   df <- get_current_data()
   ind <- which(
     vapply(
       FUN.VALUE = logical(1),
       df,
-      function(x) all(x %in% c(check_vector, NA), na.rm = TRUE) & any(x %in% check_vector),
+      function(x) inherits(x, class),
       USE.NAMES = TRUE
     ),
     useNames = TRUE
diff --git a/data/esbl_isolates.rda.no_include b/data/esbl_isolates.rda
similarity index 100%
rename from data/esbl_isolates.rda.no_include
rename to data/esbl_isolates.rda
diff --git a/man/amr-tidymodels.Rd b/man/amr-tidymodels.Rd
new file mode 100644
index 000000000..7103b651c
--- /dev/null
+++ b/man/amr-tidymodels.Rd
@@ -0,0 +1,138 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tidymodels.R
+\name{amr-tidymodels}
+\alias{amr-tidymodels}
+\alias{all_sir}
+\alias{all_sir_predictors}
+\alias{all_mic}
+\alias{all_mic_predictors}
+\alias{all_disk}
+\alias{all_disk_predictors}
+\alias{step_mic_log2}
+\alias{step_sir_numeric}
+\title{AMR Extensions for Tidymodels}
+\usage{
+all_sir()
+
+all_sir_predictors()
+
+all_mic()
+
+all_mic_predictors()
+
+all_disk()
+
+all_disk_predictors()
+
+step_mic_log2(recipe, ..., role = NA, trained = FALSE, columns = NULL,
+  skip = FALSE, id = recipes::rand_id("mic_log2"))
+
+step_sir_numeric(recipe, ..., role = NA, trained = FALSE, columns = NULL,
+  skip = FALSE, id = recipes::rand_id("sir_numeric"))
+}
+\arguments{
+\item{recipe}{A recipe object. The step will be added to the sequence of
+operations for this recipe.}
+
+\item{...}{One or more selector functions to choose variables for this step.
+See \code{\link[recipes:selections]{selections()}} for more details.}
+
+\item{role}{Not used by this step since no new variables are created.}
+
+\item{trained}{A logical to indicate if the quantities for preprocessing have
+been estimated.}
+
+\item{skip}{A logical. Should the step be skipped when the recipe is baked by
+\code{\link[recipes:bake]{bake()}}? While all operations are baked when \code{\link[recipes:prep]{prep()}} is run, some
+operations may not be able to be conducted on new data (e.g. processing the
+outcome variable(s)). Care should be taken when using \code{skip = TRUE} as it
+may affect the computations for subsequent operations.}
+
+\item{id}{A character string that is unique to this step to identify it.}
+}
+\description{
+This family of functions allows using AMR-specific data types such as \verb{<sir>} and \verb{<mic>} inside \code{tidymodels} pipelines.
+}
+\details{
+You can read more in our online \href{https://amr-for-r.org/articles/AMR_with_tidymodels.html}{AMR with tidymodels introduction}.
+
+Tidyselect helpers include:
+\itemize{
+\item \code{\link[=all_sir]{all_sir()}} and \code{\link[=all_sir_predictors]{all_sir_predictors()}} to select \code{\link[=as.sir]{<sir>}} columns
+\item \code{\link[=all_mic]{all_mic()}} and \code{\link[=all_mic_predictors]{all_mic_predictors()}} to select \code{\link[=as.mic]{<mic>}} columns
+\item \code{\link[=all_disk]{all_disk()}} and \code{\link[=all_disk_predictors]{all_disk_predictors()}} to select \code{\link[=as.disk]{<disk>}} columns
+}
+
+Pre-processing pipeline steps include:
+\itemize{
+\item \code{\link[=step_sir_numeric]{step_sir_numeric()}} to convert SIR columns to numeric (via \code{as.numeric()}), to be used with \code{\link[=all_sir_predictors]{all_sir_predictors()}}: \code{"S"} = 1, \code{"I"}/\code{"SDD"} = 2, \code{"R"} = 3. All other values are rendered \code{NA}. Keep this in mind for further processing, especially if the model does not allow for \code{NA} values.
+\item \code{\link[=step_mic_log2]{step_mic_log2()}} to convert MIC columns to numeric (via \code{as.numeric()}) and apply a log2 transform, to be used with \code{\link[=all_mic_predictors]{all_mic_predictors()}}
+}
+
+These steps integrate with \code{recipes::recipe()} and work like standard preprocessing steps. They are useful for preparing data for modelling, especially with classification models.
+}
+\examples{
+if (require("tidymodels")) {
+
+  # The below approach formed the basis for this paper: DOI 10.3389/fmicb.2025.1582703
+  # Presence of ESBL genes was predicted based on raw MIC values.
+
+
+  # example data set in the AMR package
+  esbl_isolates
+
+  # Prepare a binary outcome and convert to ordered factor
+  data <- esbl_isolates \%>\%
+    mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
+
+  # Split into training and testing sets
+  split <- initial_split(data)
+  training_data <- training(split)
+  testing_data <- testing(split)
+
+  # Create and prep a recipe with MIC log2 transformation
+  mic_recipe <- recipe(esbl ~ ., data = training_data) \%>\%
+
+    # Optionally remove non-predictive variables
+    remove_role(genus, old_role = "predictor") \%>\%
+
+    # Apply the log2 transformation to all MIC predictors
+    step_mic_log2(all_mic_predictors()) \%>\%
+
+    # And apply the preparation steps
+    prep()
+
+  # View prepped recipe
+  mic_recipe
+
+  # Apply the recipe to training and testing data
+  out_training <- bake(mic_recipe, new_data = NULL)
+  out_testing <- bake(mic_recipe, new_data = testing_data)
+
+  # Fit a logistic regression model
+  fitted <- logistic_reg(mode = "classification") \%>\%
+    set_engine("glm") \%>\%
+    fit(esbl ~ ., data = out_training)
+
+  # Generate predictions on the test set
+  predictions <- predict(fitted, out_testing) \%>\%
+    bind_cols(out_testing)
+
+  # Evaluate predictions using standard classification metrics
+  our_metrics <- metric_set(accuracy,
+                            recall,
+                            precision,
+                            sensitivity,
+                            specificity,
+                            ppv,
+                            npv)
+  metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
+
+  # Show performance
+  metrics
+}
+}
+\seealso{
+\code{\link[recipes:recipe]{recipes::recipe()}}, \code{\link[=as.sir]{as.sir()}}, \code{\link[=as.mic]{as.mic()}}, \code{\link[=as.disk]{as.disk()}}
+}
+\keyword{internal}
diff --git a/man/antibiogram.Rd b/man/antibiogram.Rd
index 222cf9554..431e8e514 100644
--- a/man/antibiogram.Rd
+++ b/man/antibiogram.Rd
@@ -209,7 +209,7 @@ wisca(your_data,
       antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
 }\if{html}{\out{</div>}}
 
-WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre datasets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).
+WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre data sets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).
 }
 }
 
diff --git a/man/clinical_breakpoints.Rd b/man/clinical_breakpoints.Rd
index ac281e683..4e31b2bc4 100644
--- a/man/clinical_breakpoints.Rd
+++ b/man/clinical_breakpoints.Rd
@@ -27,7 +27,7 @@ A \link[tibble:tibble]{tibble} with 40 217 observations and 14 variables:
 clinical_breakpoints
 }
 \description{
-Data set containing clinical breakpoints to interpret MIC and disk diffusion to SIR values, according to international guidelines. This dataset contain breakpoints for humans, 7 different animal groups, and ECOFFs.
+Data set containing clinical breakpoints to interpret MIC and disk diffusion to SIR values, according to international guidelines. This data set contains breakpoints for humans, 7 different animal groups, and ECOFFs.
 
 These breakpoints are currently implemented:
 \itemize{
diff --git a/man/esbl_isolates.Rd b/man/esbl_isolates.Rd
new file mode 100644
index 000000000..a6433c08a
--- /dev/null
+++ b/man/esbl_isolates.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{esbl_isolates}
+\alias{esbl_isolates}
+\title{Data Set with 500 ESBL Isolates}
+\format{
+A \link[tibble:tibble]{tibble} with 500 observations and 19 variables:
+\itemize{
+\item \code{esbl}\cr Logical indicator if the isolate is ESBL-producing
+\item \code{genus}\cr Genus of the microorganism
+\item \code{AMC:COL}\cr MIC values for 17 antimicrobial agents, transformed to class \code{\link{mic}} (see \code{\link[=as.mic]{as.mic()}})
+}
+}
+\usage{
+esbl_isolates
+}
+\description{
+A data set containing 500 microbial isolates with MIC values of common antibiotics and a binary \code{esbl} column for extended-spectrum beta-lactamase (ESBL) production. This data set contains randomised fictitious data but reflects reality and can be used to practise AMR-related machine learning, e.g., classification modelling with \href{https://amr-for-r.org/articles/AMR_with_tidymodels.html}{tidymodels}.
+}
+\details{
+See our \link[=amr-tidymodels]{tidymodels integration} for an example using this data set.
+}
+\examples{
+esbl_isolates
+}
+\keyword{datasets}
diff --git a/tests/testthat/test-tidymodels.R b/tests/testthat/test-tidymodels.R
new file mode 100755
index 000000000..0f5df58c1
--- /dev/null
+++ b/tests/testthat/test-tidymodels.R
@@ -0,0 +1,84 @@
+# ==================================================================== #
+# TITLE:                                                               #
+# AMR: An R Package for Working with Antimicrobial Resistance Data     #
+#                                                                      #
+# SOURCE CODE:                                                         #
+# https://github.com/msberends/AMR                                     #
+#                                                                      #
+# PLEASE CITE THIS SOFTWARE AS:                                        #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
+# https://doi.org/10.18637/jss.v104.i03                                #
+#                                                                      #
+# Developed at the University of Groningen and the University Medical  #
+# Center Groningen in The Netherlands, in collaboration with many      #
+# colleagues from around the world, see our website.                   #
+#                                                                      #
+# This R package is free software; you can freely use and distribute   #
+# it for both personal and commercial purposes under the terms of the  #
+# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
+# the Free Software Foundation.                                        #
+# We created this package for both routine data analysis and academic  #
+# research and it was publicly released in the hope that it will be    #
+# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
+#                                                                      #
+# Visit our website for the full manual and a complete tutorial about  #
+# how to conduct AMR data analysis: https://amr-for-r.org              #
+# ==================================================================== #
+
+test_that("tidymodels.R", {
+  skip_on_cran()
+
+  if (AMR:::pkg_is_available("recipes", also_load = TRUE) && AMR:::pkg_is_available("dplyr", also_load = TRUE)) {
+    # SIR
+    df <- tibble(
+      sir1 = as.sir(c("S", "I", "R", "S", "R")),
+      sir2 = as.sir(c("I", "R", "S", "R", "I")),
+      not_sir = c("S", "R", "R", "S", "I")
+    )
+    rec <- recipe(~., data = df) %>% step_sir_numeric(all_sir())
+    prepped <- prep(rec)
+    baked <- bake(prepped, new_data = df)
+    expect_inherits(baked$sir1, "numeric")
+    expect_inherits(baked$sir2, "numeric")
+    expect_equal(baked$not_sir, as.factor(df$not_sir))
+
+    # MIC
+    df <- tibble(
+      mic_col1 = as.mic(c("<=0.002", "0.002", "0.004", "0.016", "32")),
+      mic_col2 = as.mic(c("0.5", "1", "2", "4", "8")),
+      non_mic = c(1, 2, 3, 4, 5)
+    )
+    rec <- recipe(~., data = df) %>% step_mic_log2(all_mic())
+    prepped <- prep(rec)
+    baked <- bake(prepped, new_data = df)
+    expect_inherits(baked$mic_col1, "numeric")
+    expect_inherits(baked$mic_col2, "numeric")
+    expect_equal(baked$non_mic, df$non_mic)
+    expect_equal(baked$mic_col2, log2(as.numeric(df$mic_col2)))
+
+    # disk
+    df <- tibble(
+      disk_col = as.disk(c(21, 22, 23, 24, 25)),
+      non_disk = c(21, 22, 23, 24, 25)
+    )
+    rec <- recipe(~., data = df) %>% step_rm(!all_disk())
+    prepped <- prep(rec)
+    baked <- bake(prepped, new_data = df)
+    expect_inherits(baked$disk_col, "disk")
+
+    # steps check
+    df <- tibble(x = as.mic(c("1", "2", "4")))
+    rec <- recipe(~x, data = df) %>% step_mic_log2(all_mic())
+    prepped <- prep(rec)
+    tidy_df <- tidy(prepped, number = 1)
+    expect_equal(unname(tidy_df$terms), "x")
+
+    df <- tibble(x = as.sir(c("S", "I", "R")))
+    rec <- recipe(~x, data = df) %>% step_sir_numeric(all_sir())
+    prepped <- prep(rec)
+    tidy_df <- tidy(prepped, number = 1)
+    expect_equal(unname(tidy_df$terms), "x")
+  }
+})
diff --git a/vignettes/AMR_with_tidymodels.Rmd b/vignettes/AMR_with_tidymodels.Rmd
index 1f3fe87e1..9e6b2da02 100644
--- a/vignettes/AMR_with_tidymodels.Rmd
+++ b/vignettes/AMR_with_tidymodels.Rmd
@@ -22,7 +22,7 @@ knitr::opts_chunk$set(
 )
 ```
 
-> This page was entirely written by our [AMR for R Assistant](https://chat.amr-for-r.org), a ChatGPT manually-trained model able to answer any question about the `AMR` package.
+> This page was almost entirely written by our [AMR for R Assistant](https://chat.amr-for-r.org), a ChatGPT manually-trained model able to answer any question about the `AMR` package.
 
 Antimicrobial resistance (AMR) is a global health crisis, and understanding resistance patterns is crucial for managing effective treatments. The `AMR` R package provides robust tools for analysing AMR data, including convenient antimicrobial selector functions like `aminoglycosides()` and `betalactams()`. 
 
@@ -219,18 +219,163 @@ This workflow is extensible to other antimicrobial classes and resistance patter
 
 In this second example, we demonstrate how to use `<mic>` columns directly in `tidymodels` workflows using AMR-specific recipe steps. This includes a transformation to `log2` scale using `step_mic_log2()`, which prepares MIC values for use in classification models.
 
-This approach and idea formed the basis for the publication [DOI: 10.3389/fmicb.2025.1582703](https://doi.org/10.3389/fmicb.2025.1582703) to model the presence of extended-spectrum beta-lactamases (ESBL).
+This approach and idea formed the basis for the publication [DOI: 10.3389/fmicb.2025.1582703](https://doi.org/10.3389/fmicb.2025.1582703) to model the presence of extended-spectrum beta-lactamases (ESBL) based on MIC values.
 
-> NOTE: THIS EXAMPLE WILL BE AVAILABLE IN A NEXT VERSION (#TODO)
-> 
-> The new AMR package version will contain new tidymodels selectors such as `step_mic_log2()`.
+### **Objective**
 
-<!-- TODO for AMR v3.1.0: add info from here: https://github.com/msberends/AMR/blob/2461631bcefa78ebdb37bdfad359be74cdd9165a/vignettes/AMR_with_tidymodels.Rmd#L212-L291 -->
+Our goal is to:
+
+1. Use raw MIC values to predict whether a bacterial isolate produces ESBL.
+2. Apply AMR-aware preprocessing in a `tidymodels` recipe.
+3. Train a classification model and evaluate its predictive performance.
+
+### **Data Preparation**
+
+We use the `esbl_isolates` dataset that comes with the AMR package.
+
+```{r}
+# Load required libraries
+library(AMR)
+library(tidymodels)
+
+# View the esbl_isolates data set
+esbl_isolates
+
+# Prepare a binary outcome and convert to ordered factor
+data <- esbl_isolates %>%
+  mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
+```
+
+**Explanation:**
+
+- `esbl_isolates`: Contains MIC test results and ESBL status for each isolate.
+- `mutate(esbl = ...)`: Converts the target column to an ordered factor for classification.
+
+### **Defining the Workflow**
+
+#### 1. Preprocessing with a Recipe
+
+We use our `step_mic_log2()` function to log2-transform MIC values, ensuring that MICs are numeric and properly scaled. All MIC predictors can easily and agnostically selected using the new `all_mic_predictors()`:
+
+```{r}
+# Split into training and testing sets
+set.seed(123)
+split <- initial_split(data)
+training_data <- training(split)
+testing_data <- testing(split)
+
+# Define the recipe
+mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
+  remove_role(genus, old_role = "predictor") %>%  # Remove non-informative variable
+  step_mic_log2(all_mic_predictors()) #%>%         # Log2 transform all MIC predictors
+ # prep()
+
+mic_recipe
+```
+
+**Explanation:**
+
+- `remove_role()`: Removes irrelevant variables like genus.
+- `step_mic_log2()`: Applies `log2(as.numeric(...))` to all MIC predictors in one go.
+- `prep()`: Finalises the recipe based on training data.
+
+#### 2. Specifying the Model
+
+We use a simple logistic regression to model ESBL presence, though recent models such as xgboost ([link to `parsnip` manual](https://parsnip.tidymodels.org/reference/details_boost_tree_xgboost.html)) could be much more precise.
+
+```{r}
+# Define the model
+model <- logistic_reg(mode = "classification") %>%
+  set_engine("glm")
+
+model
+```
+
+**Explanation:**
+
+- `logistic_reg()`: Specifies a binary classification model.
+- `set_engine("glm")`: Uses the base R GLM engine.
+
+#### 3. Building the Workflow
+
+```{r}
+# Create workflow
+workflow_model <- workflow() %>%
+  add_recipe(mic_recipe) %>%
+  add_model(model)
+
+workflow_model
+```
+
+### **Training and Evaluating the Model**
+
+```{r}
+# Fit the model
+fitted <- fit(workflow_model, training_data)
+
+# Generate predictions
+predictions <- predict(fitted, testing_data) %>%
+  bind_cols(predict(fitted, out_testing, type = "prob")) %>% # add probabilities
+  bind_cols(testing_data)
+
+# Evaluate model performance
+our_metrics <- metric_set(accuracy, recall, precision, sensitivity, specificity, ppv, npv)
+metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
+
+metrics
+```
+
+**Explanation:**
+
+- `fit()`: Trains the model on the processed training data.
+- `predict()`: Produces predictions for unseen test data.
+- `metric_set()`: Allows evaluating multiple classification metrics. This will make `our_metrics` to become a function that we can use to check the predictions with.
+
+It appears we can predict ESBL gene presence with a positive predictive value (PPV) of `r round(metrics[metrics$.metric == "ppv", ]$.estimate, 3) * 100`% and a negative predictive value (NPV) of `r round(metrics[metrics$.metric == "npv", ]$.estimate, 3) * 100` using a simplistic logistic regression model.
+
+### **Visualising Predictions**
+
+We can visualise predictions by comparing predicted and actual ESBL status.
+
+```{r}
+library(ggplot2)
+
+ggplot(predictions, aes(x = esbl, fill = .pred_class)) +
+  geom_bar(position = "stack") +
+  labs(title = "Predicted vs Actual ESBL Status",
+       x = "Actual ESBL",
+       y = "Count") +
+  theme_minimal()
+```
+
+And plot the certainties too - how certain were the actual predictions?
+
+```{r}
+predictions %>%
+  mutate(certainty = ifelse(.pred_class == "FALSE",
+                            .pred_FALSE,
+                            .pred_TRUE),
+         correct = ifelse(esbl == .pred_class, "Right", "Wrong")) %>%
+  ggplot(aes(x = seq_len(nrow(predictions)),
+             y = certainty,
+             colour = correct)) +
+  scale_colour_manual(values = c(Right = "green3", Wrong = "red2"),
+                      name = "Correct?") +
+  geom_point() + 
+  scale_y_continuous(labels = function(x) paste0(x * 100, "%"),
+                     limits = c(0.5, 1)) +
+  theme_minimal()
+```
+### **Conclusion**
+
+In this example, we showcased how the new `AMR`-specific recipe steps simplify working with `<mic>` columns in `tidymodels`. The `step_mic_log2()` transformation converts ordered MICs to log2-transformed numerics, improving compatibility with classification models.
+
+This pipeline enables realistic, reproducible, and interpretable modelling of antimicrobial resistance data.
 
 ---
 
 
-## Example 2: Predicting AMR Over Time
+## Example 3: Predicting AMR Over Time
 
 In this third example, we aim to predict antimicrobial resistance (AMR) trends over time using `tidymodels`. We will model resistance to three antibiotics (amoxicillin `AMX`, amoxicillin-clavulanic acid `AMC`, and ciprofloxacin `CIP`), based on historical data grouped by year and hospital ward.