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.
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 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 | 0.000 |
| 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 | 0.001 |
| 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".