Pilot study of an online hospital antibiotic use tracking and reporting system
Bibliographic record
Abstract
Background: Antimicrobial use (AMU) varies widely among hospitals, suggesting a need to better monitor usage and evaluate the effectiveness of antimicrobial stewardship programs (ASPs). Our objective was to assess the feasibility of implementing an online voluntary hospital antibiotic use tracking and reporting system. Methods: An online survey was sent to ASP clinicians representing hospitals across Ontario. Hospitals that tracked total hospital-wide inpatient antibiotic use in 2017 were asked to submit either days of therapy (DOT) or defined daily doses (DDD), along with separate inpatient days (PD), which were used as the denominator. Respondents who indicated no hospital-wide AMU tracking were asked to describe the barriers to its use. Antibiotic use was displayed on a public website for consenting hospitals. Results: Of 201 eligible hospitals, 66 (33%) provided AMU data representing 10,634 of 25,208 (43%) eligible inpatient beds in the province. DOT and DDD data were provided by 36 hospitals, each. Weighted average antibiotic use was highest in acute teaching hospitals (513 DOT/1,000 PD, 709 DDD/1,000 PD) and lowest in complex continuing care and rehabilitation facilities (158 DOT/1,000 PD, 159 DDD/1,000 PD). Barriers cited for providing hospital-wide AMU data include lack of time and resources to collect and evaluate AMU data and technological limitations preventing data collection. Conclusion: Integrating hospital AMU tracking and reporting as part of a voluntary initiative is feasible, with relatively broad participation. Short of a legislative mandate for participation, opportunities still exist to increase representation, including provision of guidance and technical support to help hospitals track and share AMU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".