Trends in Outpatient Antibiotic Prescriptions in the United States During the COVID-19 Pandemic in 2020
Bibliographic record
Abstract
Statistical significance was set at P < .05.STATA/SE version 16.1 (StataCorp) was used for statistical analysis. ResultsIn quarter 1 of 2020 (ie, January-March), the mean monthly prescription fills of each agent did not significantly differ from anticipated values (Table ).In quarter 2 (ie, April-June), quarter 3 (ie, July-September), and quarter 4 (ie, October-December 2020), the mean monthly amoxicillin, amoxicillin-clavulanate, and doxycycline fills were significantly lower than anticipated (amoxicillin: 25.3%-44.1%;P = .003-.03; amoxicillin-clavulanate: 30.1%-40.0%;P = .005-.02; doxycycline: 17.8%-20.7%;P = .008-.01).Azithromycin fills were significantly lower than anticipated in quarters 2 and quarter 4 (quarter 2: 35.4% decrease; P = .02;quarter 4: 31.8%decrease; P = .03).The decrease in quarter 3 was not significant (22.1% decrease; P = .06).Mean monthly cephalexin fills were 10.1% lower than anticipated in quarter 4 (P = .05);those of clindamycin were 4.8% lower than anticipated in quarter 3 (P = .04).Mean monthly ciprofloxacin fills were 12.2% and 14.3% higher than anticipated in quarters 3 and 4, respectively (P = .001and P = .04).Nitrofurantoin and levofloxacin fills were not significantly different from projections in quarter 2, 3, and 4.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".