Development of a provincial interactive antibiogram tool for Ontario
Bibliographic record
Abstract
Background: Antimicrobial resistance (AMR) is a public health issue with significant impact on health care. Antibiogram development and deployment is a key strategy for managing and preventing AMR. Our objective was to develop an Ontario antibiogram as part of a larger provincial initiative aimed at advancing antimicrobial stewardship in the province. Methods: As part of a voluntary provincial online survey, antibiogram data from 100 of 201 (49.8%) Ontario hospitals were collected and included. All hospitals in Ontario were eligible to participate except those providing only mental health or ambulatory services. Weighted provincial and regional antibiotic susceptibilities (percentages) were conducted using descriptive statistical analyses, and an interactive antibiogram spreadsheet was developed. Respondent-identified barriers to collecting and interpreting antibiogram data are presented descriptively. Results: There was wide regional variability in antimicrobial-resistant organisms across Ontario. Provincial methicillin-resistant Staphylococcus aureus prevalence was 24.6%, ranging from 5.9% to 43.7% regionally. Provincial Escherichia coli resistance to ceftriaxone and ciprofloxacin was 13.8% (regional range 6.0%–25.1%) and 22.5% (regional range 9.8–37.8%), respectively. Klebsiella spp resistance to ceftriaxone and ciprofloxacin was similar across all health regions, with overall provincial rates of 7.5% and 5.6%, respectively. Conclusions: We have demonstrated that integrating hospital AMR tracking and reporting as part of a larger voluntary provincial antimicrobial stewardship program initiative is a feasible approach to capturing AMR data. The provincial antibiogram serves as a benchmark for the current state of AMR provincially and across health regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".