Postdated versus usual delayed antibiotic prescriptions in primary care: Reduction in antibiotic use for acute respiratory infections?
Bibliographic record
Abstract
OBJECTIVE: To determine whether postdating delayed antibiotic prescriptions results in a further decrease (over usual delayed prescriptions) in antibiotic use. DESIGN: Randomized controlled trial. SETTING: A small rural town in Newfoundland and Labrador. PARTICIPANTS: A total of 149 consecutive adult primary care patients who presented with acute upper respiratory tract infections. INTERVENTION: Delayed prescriptions for patients who might require antibiotics were randomly dated either the day of the office visit (ie, the usual group) or 2 days later (ie, the postdated group). MAIN OUTCOME MEASURES: Whether or not the prescriptions were filled and the time it took for the patients to fill the prescriptions were noted by the 4 local pharmacies and relayed to the investigators. RESULTS: In total, 149 delayed antibiotic prescriptions were written, 1 per patient. Of the 74 usual delayed prescriptions given out, 32 (43.2%) were filled; of the 75 postdated delayed prescriptions given out, 33 (44.0%) were filled. Sixteen patients from each group filled their delayed prescriptions earlier than the recommended 48 hours. Statistical analyses-χ² tests to compare the rates of antibiotic use between the 2 groups and t tests to compare the mean time to fill the prescription between the 2 groups-indicated that these results were not significant (P > .05). CONCLUSION: Although delayed prescriptions reduce the rate of antibiotic use, postdating the delayed prescription does not seem to lead to further reduction in use.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".