Evaluating Antimicrobial Use and Spectrum of Activity in Ontario Hospitals: Feasibility of a Multicentered Point Prevalence Study
Bibliographic record
Abstract
BACKGROUND: Antimicrobial stewardship, a key component of an overall strategy to address antimicrobial resistance, has been recognized as a global priority. The ability to track and benchmark antimicrobial use (AMU) is critical to advancing stewardship from an organizational and provincial perspective. As there are few comprehensive systems in Canada that allow for benchmarking, Public Health Ontario conducted a pilot in 2016/2017 to assess the feasibility of using a point prevalence methodology as the basis of a province-wide AMU surveillance program. METHODS: Three acute care hospitals of differing sizes in Ontario, Canada, participated. Adults admitted to inpatient acute care beds on the survey date were eligible for inclusion; a sample size of 170 per hospital was targeted, and data were collected for the 24-hour period before and including the survey date. Debrief sessions at each site were used to gather feedback about the process. Prevalence of AMU and the Antimicrobial Spectrum Index (ASI) was reported for each hospital and by indication per patient case. RESULTS: Participants identified required improvements for scalability including streamlining ethics, data sharing processes, and enhancing the ability to compare with peer organizations at a provincial level. Of 457 patients, 172 (38%) were receiving at least 1 antimicrobial agent. Beta-lactam/beta-lactamase inhibitors were the most common (18%). The overall mean ASI per patient was 6.59; most cases were for treatment of infection (84%). CONCLUSIONS: This pilot identified factors and features required for a scalable provincial AMU surveillance program; future efforts should harmonize administrative processes and enable interfacility benchmarking.
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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 | 0.027 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".