Trends in estimated total retail dispensed prescriptions of purported COVID-19 treatments and preventions in Canada
Bibliographic record
Abstract
Abstract Objectives Several medications were proposed for the treatment and prophylaxis of COVID-19 but with limited supporting evidence. Herein, we assessed trends in the volume of projected total retail dispensed prescriptions for 12 agents proposed for treatment and prevention of COVID-19 before and after March 2020 in Canada. Methods We conducted a cross-sectional study using monthly prescription volumes obtained from IQVIA’s CompuScript database. We used joinpoint regression to identify significant inflection points and calculate the monthly percent change (MPC). Key findings Dispensations peaked after March 2020 for several medications, including hydroxychloroquine, fluvoxamine, ivermectin, colchicine, tocilizumab, sarilumab and famotidine. Although most peaks were short lived, large increases were observed for ivermectin (MPC from September 2020 to January 2021 = 28%) and famotidine (MPC from June 2021 to October 2021 = 14%). Conclusions Overall, Canadian prescribing patterns were mostly consistent with recommendations from guidelines and health regulatory bodies. Nonetheless, active monitoring of trends should continue.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".