Population-Wide Peer Comparison Audit and Feedback to Reduce Antibiotic Initiation and Duration in Long-Term Care Facilities with Embedded Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Antibiotic overprescribing in long-term care settings is driven by prescriber preferences and is associated with preventable harms for residents. We aimed to determine whether peer comparison audit and feedback reporting for physicians reduces antibiotic overprescribing among residents. METHODS: We employed a province wide, difference-in-differences study of antibiotic prescribing audit and feedback, with an embedded pragmatic randomized controlled trial (RCT) across all long-term care facilities in Ontario, Canada, in 2019. The study year included 1238 physicians caring for 96 185 residents. In total, 895 (72%) physicians received no feedback; 343 (28%) were enrolled to receive audit and feedback and randomized 1:1 to static or dynamic reports. The primary outcomes were proportion of residents initiated on an antibiotic and proportion of antibiotics prolonged beyond 7 days per quarter. RESULTS: Among all residents, between the first quarter of 2018 and last quarter of 2019, there were temporal declines in antibiotic initiation (28.4% to 21.3%) and prolonged duration (34.4% to 29.0%). Difference-in-differences analysis confirmed that feedback was associated with a greater decline in prolonged antibiotics (adjusted difference -2.65%, 95% confidence interval [CI]: -4.93 to -.28%, P = .026), but there was no significant difference in antibiotic initiation. The reduction in antibiotic durations was associated with 335 912 fewer days of treatment. The embedded RCT detected no differences in outcomes between the dynamic and static reports. CONCLUSIONS: Peer comparison audit and feedback is a pragmatic intervention that can generate small relative reductions in the use of antibiotics for prolonged durations that translate to large reductions in antibiotic days of treatment across populations. Clinical Trials Registration. NCT03807466.
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 | 0.024 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".