diff --git a/DESCRIPTION b/DESCRIPTION
index bd9c1f2a6..c57bafc5e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.1.1.9251
-Date: 2025-04-21
+Version: 2.1.1.9252
+Date: 2025-04-25
 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/NEWS.md b/NEWS.md
index a406737c1..e125022a3 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,4 @@
-# AMR 2.1.1.9251
+# AMR 2.1.1.9252
 
 *(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://amr-for-r.org/#get-this-package).)*
 
diff --git a/R/aa_amr-package.R b/R/aa_amr-package.R
index 57e355b79..98dd21290 100755
--- a/R/aa_amr-package.R
+++ b/R/aa_amr-package.R
@@ -32,7 +32,7 @@
 #' @description
 #' Welcome to the `AMR` package.
 #'
-#' The `AMR` package is a [free and open-source](https://amr-for-r.org/#copyright) R package with [zero dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. [Many different researchers](https://amr-for-r.org/authors.html) from around the globe are continually helping us to make this a successful and durable project!
+#' The `AMR` package is a peer-reviewed, [free and open-source](https://amr-for-r.org/#copyright) R package with [zero dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. [Many different researchers](https://amr-for-r.org/authors.html) from around the globe are continually helping us to make this a successful and durable project!
 #'
 #' This work was published in the Journal of Statistical Software (Volume 104(3); \doi{10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}).
 #'
diff --git a/R/guess_ab_col.R b/R/guess_ab_col.R
index 9d6240a19..983fc678a 100755
--- a/R/guess_ab_col.R
+++ b/R/guess_ab_col.R
@@ -47,7 +47,6 @@
 #' guess_ab_col(df, "J01AA07") # ATC code of tetracycline
 #'
 #' guess_ab_col(df, "J01AA07", verbose = TRUE)
-#' # NOTE: Using column 'tetr' as input for J01AA07 (tetracycline).
 #'
 #' # WHONET codes
 #' df <- data.frame(
@@ -56,7 +55,7 @@
 #' )
 #' guess_ab_col(df, "ampicillin")
 #' guess_ab_col(df, "J01CR02")
-#' guess_ab_col(df, as.ab("augmentin"))
+#' guess_ab_col(df, "augmentin")
 guess_ab_col <- function(x = NULL, search_string = NULL, verbose = FALSE, only_sir_columns = FALSE) {
   meet_criteria(x, allow_class = "data.frame", allow_NULL = TRUE)
   meet_criteria(search_string, allow_class = "character", has_length = 1, allow_NULL = TRUE)
diff --git a/R/plotting.R b/R/plotting.R
index 8ff500dc2..42e67e580 100755
--- a/R/plotting.R
+++ b/R/plotting.R
@@ -412,7 +412,7 @@ create_scale_sir <- function(aesthetics, colours_SIR, language, eucast_I, ...) {
         x[x == "I"] <- "(I) Intermediate"
       }
       x[x == "R"] <- "(R) Resistant"
-      x[x == "NI"] <- "(NI) Not interpretable"
+      x[x == "NI"] <- "(NI) Non-interpretable"
       x <- translate_AMR(x, language = language)
     }
     x
@@ -685,7 +685,8 @@ autoplot.mic <- function(object,
       "(SDD) Susceptible dose-dependent" = colours_SIR[2],
       "(I) Susceptible, incr. exp." = colours_SIR[2],
       "(I) Intermediate" = colours_SIR[2],
-      "(R) Resistant" = colours_SIR[3]
+      "(R) Resistant" = colours_SIR[3],
+      "(NI) Non-interpretable" = "grey"
     )
     names(vals) <- translate_into_language(names(vals), language = language)
     p <- p +
@@ -918,7 +919,8 @@ autoplot.disk <- function(object,
       "(SDD) Susceptible dose-dependent" = colours_SIR[2],
       "(I) Susceptible, incr. exp." = colours_SIR[2],
       "(I) Intermediate" = colours_SIR[2],
-      "(R) Resistant" = colours_SIR[3]
+      "(R) Resistant" = colours_SIR[3],
+      "(NI) Non-interpretable" = "grey"
     )
     names(vals) <- translate_into_language(names(vals), language = language)
     p <- p +
diff --git a/R/sir.R b/R/sir.R
index c80a9b161..08d5a0acc 100755
--- a/R/sir.R
+++ b/R/sir.R
@@ -323,7 +323,7 @@
 #' )
 #'
 #'
-#' # For CLEANING existing SIR values ------------------------------------
+#' # For CLEANING existing SIR values -------------------------------------
 #'
 #' as.sir(c("S", "SDD", "I", "R", "NI", "A", "B", "C"))
 #' as.sir("<= 0.002; S") # will return "S"
@@ -1263,11 +1263,16 @@ as_sir_method <- function(method_short,
   )
   if (method == "mic") {
     if (any(guideline_coerced %like% "CLSI")) {
+      # CLSI in log 2 ----
       # CLSI says: if MIC is not a log2 value it must be rounded up to the nearest log2 value
-      log2_levels <- 2^c(-9:12)
-      df$values[which(df$guideline %like% "CLSI")] <- vapply(
-        FUN.VALUE = character(1),
-        df$values[which(df$guideline %like% "CLSI")],
+      log2_levels <- as.double(VALID_MIC_LEVELS[which(VALID_MIC_LEVELS %in% 2^c(-20:20))])
+      test_values <- df$values[which(df$guideline %like% "CLSI")]
+      test_values_dbl <- as.double(test_values)
+      test_values_dbl[test_values %like% "^>[0-9]"] <- test_values_dbl[test_values %like% "^>[0-9]"] + 0.0000001
+      test_values_dbl[test_values %like% "^<[0-9]"] <- test_values_dbl[test_values %like% "^>[0-9]"] - 0.0000001
+      test_outcome <- vapply(
+        FUN.VALUE = double(1),
+        test_values_dbl,
         function(mic_val) {
           if (is.na(mic_val)) {
             return(NA_character_)
@@ -1278,13 +1283,14 @@ as_sir_method <- function(method_short,
               if (message_not_thrown_before("as.sir", "CLSI", "MICupscaling")) {
                 warning_("Some MICs were converted to the nearest higher log2 level, following the CLSI interpretation guideline.")
               }
-              return(as.character(log2_val)) # will be MIC later
+              return(as.double(log2_val)) # will be MIC later
             } else {
-              return(as.character(mic_val))
+              return(as.double(mic_val))
             }
           }
         }
       )
+      df$values[which(df$guideline %like% "CLSI" & test_values != test_outcome)] <- test_outcome[which(test_values != test_outcome)]
     }
     df$values <- as.mic(df$values)
   } else if (method == "disk") {
@@ -1430,10 +1436,6 @@ as_sir_method <- function(method_short,
         mo_current_other
       ))
 
-
-    # TODO are operators considered??
-    # This seems to not work well: as.sir(as.mic(c(4, ">4", ">=4", 8, ">8", ">=8")), ab = "AMC", mo = "E. coli", breakpoint_type = "animal", host = "dogs", guideline = "CLSI 2024")
-
     if (breakpoint_type == "animal") {
       # 2025-03-13 for now, only strictly follow guideline for current host, no extrapolation
       breakpoints_current <- breakpoints_current[which(breakpoints_current$host == host_current), , drop = FALSE]
@@ -1619,7 +1621,17 @@ as_sir_method <- function(method_short,
         ),
         "\n",
         ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^[><]=[0-9]" & as.double(values) > breakpoints_current$breakpoint_S & as.double(values) < breakpoints_current$breakpoint_R,
-          paste0("MIC values within the breakpoint guideline range with the operator '<=' or '>=' are considered 'NI' since capped_mic_handling = \"", capped_mic_handling, "\""),
+          paste0("MIC values within the breakpoint guideline range with the operator '<=' or '>=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\""),
+          ""
+        ),
+        "\n",
+        ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^<=[0-9]" & as.double(values) == breakpoints_current$breakpoint_R,
+          paste0("MIC values at the R breakpoint with the operator '<=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\""),
+          ""
+        ),
+        "\n",
+        ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^>=[0-9]" & as.double(values) == breakpoints_current$breakpoint_S,
+          paste0("MIC values at the S breakpoint with the operator '>=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\""),
           ""
         )
       )
@@ -1642,6 +1654,8 @@ as_sir_method <- function(method_short,
           capped_mic_handling %in% c("conservative", "inverse") & as.character(values_bak) %like% "^[<][0-9]" ~ as.sir("S"),
           capped_mic_handling %in% c("conservative", "inverse") & as.character(values_bak) %like% "^[>][0-9]" ~ as.sir("R"),
           capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^[><]=[0-9]" & as.double(values) > breakpoints_current$breakpoint_S & as.double(values) < breakpoints_current$breakpoint_R ~ as.sir("NI"),
+          capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^<=[0-9]" & as.double(values) == breakpoints_current$breakpoint_R ~ as.sir("NI"),
+          capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^>=[0-9]" & as.double(values) == breakpoints_current$breakpoint_S ~ as.sir("NI"),
           values <= breakpoints_current$breakpoint_S ~ as.sir("S"),
           guideline_current %like% "EUCAST" & values > breakpoints_current$breakpoint_R ~ as.sir("R"),
           guideline_current %like% "CLSI" & values >= breakpoints_current$breakpoint_R ~ as.sir("R"),
diff --git a/R/sysdata.rda b/R/sysdata.rda
index 502ea6581..688bd6fcd 100755
Binary files a/R/sysdata.rda and b/R/sysdata.rda differ
diff --git a/data-raw/translations.tsv b/data-raw/translations.tsv
index f56f3a622..aebe146ab 100644
--- a/data-raw/translations.tsv
+++ b/data-raw/translations.tsv
@@ -45,7 +45,7 @@ susceptible, incr. exp.	FALSE	TRUE	FALSE	FALSE	易感，接触增加	Vnímavý,
 Susceptible	TRUE	FALSE	FALSE	FALSE	易受影响	Vnímavý	Modtagelig	Gevoelig	Altis	Sensible	Empfindlich	Ευαίσθητο	Sensibile	影響を受けやすい	Mottakelig	Podatny	Suscetível	Susceptibil	Чутливий	Susceptible	Mottaglig	Duyarlı	Чутливий
 Incr. exposure	TRUE	FALSE	FALSE	FALSE	暴露增加	Zvýšená expozice	Øget eksponering	Verhoogde blootstelling	Lisääntynyt altistuminen	Exposition accrue	Erhöhte Belastung	Αυξημένη έκθεση	Esposizione aumentata	曝露量増加	Økt eksponering	Większe narażenie	Exposição aumentada	Expunere crescută	Підвищена експозиція	Mayor exposición	Ökad exponering	Artmış maruziyet	Підвищена експозиція
 Resistant	TRUE	FALSE	FALSE	FALSE	耐药性	Rezistentní	Resistent	Resistent	Kestävä	Résistant	Resistent	Ανθεκτικός	Resistente	耐性	Resistent	Odporny	Resistente	Rezistent	Стійкий	Resistente	Resistent	Dayanıklı	Стійкий
-Not interpretable	TRUE	FALSE	FALSE	FALSE	无法解释	Nelze interpretovat	Ufortolkelig	Niet interpreteerbaar	Ei tulkittavissa	Non interprétable	Nicht interpretierbar	Μη ερμηνεύσιμο	Non interpretabile	解釈不可	Utolkelig ikke	Niemożliwe do interpretacji	Não interpretável	Neinterpretabil	Непереводимо	No interpretable	Inte tolkningsbar	Yorumlanamaz	Непридатний до інтерпретації
+Non-interpretable	TRUE	FALSE	FALSE	FALSE	无法解释	Nelze interpretovat	Ufortolkelig	Niet interpreteerbaar	Ei tulkittavissa	Non interprétable	Nicht interpretierbar	Μη ερμηνεύσιμο	Non interpretabile	解釈不可	Utolkelig ikke	Niemożliwe do interpretacji	Não interpretável	Neinterpretabil	Непереводимо	No interpretable	Inte tolkningsbar	Yorumlanamaz	Непридатний до інтерпретації
 antibiotic	TRUE	TRUE	FALSE	FALSE	抗生素	antibiotikum	antibiotikum	antibioticum	antibiootti	antibiotique	Antibiotikum	αντιβιοτικό	antibiotico	抗生物質	Antibiotikum	antybiotyk	antibiótico	antibiotic	антибиотик	antibiótico	antibiotika	Antibiyotik	антибіотик
 Antibiotic	TRUE	TRUE	FALSE	FALSE	抗生素	Antibiotikum	Antibiotikum	Antibioticum	Antibiootti	Antibiotique	Antibiotikum	Αντιβιοτικό	Antibiotico	抗生物質	Antibiotikum	Antybiotyk	Antibiótico	Antibiotic	Антибиотик	Antibiótico	Antibiotika	Antibiyotik	Антибіотик
 Drug	TRUE	TRUE	FALSE	FALSE	药物	Lék	Lægemiddel	Middel	Lääke	Médicament	Medikament	Φάρμακο	Droga	薬剤	Legemiddel	Lek	Droga	Medicament	Лекарство	Fármaco	Läkemedel	İlaç	Лікарський засіб
diff --git a/man/AMR.Rd b/man/AMR.Rd
index 4dc6837df..1540b0d40 100644
--- a/man/AMR.Rd
+++ b/man/AMR.Rd
@@ -28,7 +28,7 @@ A BibTeX entry for LaTeX users is:
 \description{
 Welcome to the \code{AMR} package.
 
-The \code{AMR} package is a \href{https://amr-for-r.org/#copyright}{free and open-source} R package with \href{https://en.wikipedia.org/wiki/Dependency_hell}{zero dependencies} to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. \strong{Our aim is to provide a standard} for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. \href{https://amr-for-r.org/authors.html}{Many different researchers} from around the globe are continually helping us to make this a successful and durable project!
+The \code{AMR} package is a peer-reviewed, \href{https://amr-for-r.org/#copyright}{free and open-source} R package with \href{https://en.wikipedia.org/wiki/Dependency_hell}{zero dependencies} to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. \strong{Our aim is to provide a standard} for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. \href{https://amr-for-r.org/authors.html}{Many different researchers} from around the globe are continually helping us to make this a successful and durable project!
 
 This work was published in the Journal of Statistical Software (Volume 104(3); \doi{10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}).
 
diff --git a/man/as.sir.Rd b/man/as.sir.Rd
index 40c560056..439501f2d 100644
--- a/man/as.sir.Rd
+++ b/man/as.sir.Rd
@@ -403,7 +403,7 @@ as.sir(
 )
 
 
-# For CLEANING existing SIR values ------------------------------------
+# For CLEANING existing SIR values -------------------------------------
 
 as.sir(c("S", "SDD", "I", "R", "NI", "A", "B", "C"))
 as.sir("<= 0.002; S") # will return "S"
diff --git a/man/guess_ab_col.Rd b/man/guess_ab_col.Rd
index eab6d76d0..1ae56226a 100644
--- a/man/guess_ab_col.Rd
+++ b/man/guess_ab_col.Rd
@@ -35,7 +35,6 @@ guess_ab_col(df, "amoxicillin")
 guess_ab_col(df, "J01AA07") # ATC code of tetracycline
 
 guess_ab_col(df, "J01AA07", verbose = TRUE)
-# NOTE: Using column 'tetr' as input for J01AA07 (tetracycline).
 
 # WHONET codes
 df <- data.frame(
@@ -44,5 +43,5 @@ df <- data.frame(
 )
 guess_ab_col(df, "ampicillin")
 guess_ab_col(df, "J01CR02")
-guess_ab_col(df, as.ab("augmentin"))
+guess_ab_col(df, "augmentin")
 }