Exploring variability in antibiograms: a cross-sectional study
Bibliographic record
Abstract
Abstract Background Antibiograms are important tools for guiding empirical antimicrobial prescribing and monitoring antimicrobial resistance (AMR); however, there are challenges to their implementation and interpretation in practice. Variable formatting may be a contributing factor. This study explores variability in antibiogram data presentation to identify opportunities for improvement. Methods Antibiograms from hospitals in Ontario were evaluated by visual inspection for general formatting and style, organism-specific data presentation and stratification based on CLSI M39 guidelines (Fifth Edition, 2022) and relevant literature. Hospitals were categorized by type and descriptive analysis was performed. Results Forty-three antibiograms from 60 hospitals were included: 33.3% were large community; 26.7% were academic teaching; 20% were small community; 11.7% were medium community; and 8.5% were complex continuing care/rehabilitation facilities. All antibiograms reported at least 1 year of data, with 26.5% aggregating data from multiple facilities. Most either reported on organisms with at least 30 isolates (23.2%) or included a statement about interpretation of small numbers (69.8%). Only 27.9% included a statement about exclusion of duplicates, and 18.6% included guidance on how to use the antibiogram. Data were reported separately for Staphylococcus aureus, MRSA and MSSA in 39.5% of antibiograms. Almost half of antibiograms incorporated at least one method of stratification; specimen source was most common (39.5%); and 18.6% (n = 8) included a weighted-incidence syndromic combination antibiogram (WISCA). Conclusions There is significant variability in antibiogram data presentation across Ontario hospitals. Additional format standardization may help improve use for clinical decision-making and monitoring of AMR trends.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".