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reference/antibiogram.md
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@@ -427,9 +427,9 @@ pathogen, but rather by syndrome.
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).
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
@@ -777,9 +777,9 @@ antibiogram(example_isolates,
#> # Type: WISCA with 95% CI
#> `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹
#> <chr> <chr> <chr>
#> 1 Clinical 73.4% (67.6-78.6%) 92.4% (90.6-93.7%)
#> 2 ICU 57.4% (49.7-65.6%) 85% (82.1-87.6%)
#> 3 Outpatient 56.9% (46.9-66.7%) 74.4% (69-79.7%)
#> 1 Clinical 73.5% (68.1-78.6%) 92.3% (90.8-93.8%)
#> 2 ICU 57.3% (49.8-64.9%) 84.8% (82-87.7%)
#> 3 Outpatient 56.8% (47-67%) 74.4% (68.6-79.7%)
#> # ℹ abbreviated name: ¹​`Piperacillin/tazobactam + Gentamicin`
#> # ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` <chr>
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
@@ -804,9 +804,9 @@ if (requireNamespace("knitr")) {
#>
#> |Syndromic Group |Piperacillin/tazobactam |
#> |:---------------|:-----------------------|
#> |Clinical |73.6% (68.4-79%) |
#> |ICU |57.4% (49.7-65.4%) |
#> |Outpatient |57% (47.2-66.7%) |
#> |Clinical |73.6% (68.5-78.7%) |
#> |ICU |57.4% (49.3-65.8%) |
#> |Outpatient |57% (47.1-67.3%) |
# Generate plots with ggplot2 or base R --------------------------------