The Effect of Antidepressant Warnings on Prescribing Trends in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: We studied whether 5 regulatory agency advisories concerning the possible increased risk of suicidal behavior during antidepressant therapy had an effect on antidepressant prescription trends in Ontario. METHODS: We conducted a population-based, time-series analysis of new monthly antidepressant prescriptions dispensed by the Ontario Drug Benefits program in 3 age groups (younger than 20 years, 20-65 years old, and older than 65 years) over a 7-year period (April 1998 to March 2005). The impact of five advisories about the possible risk of suicide during antidepressant therapy was also analyzed. RESULTS: The number of new prescriptions for selective serotonin reuptake inhibitors as a group did not change after any antidepressant warning in any age group. However, the rate of new paroxetine prescriptions in patients younger than 20 years declined by 54% immediately after the first warning for paroxetine was issued in the United Kingdom in June 2003. That same warning had no effect on new paroxetine prescriptions in the other age categories. CONCLUSION: The warning about paroxetine use in depressed patients younger than 18 years that was issued in the United Kingdom led to a significant decrease in new paroxetine prescriptions for young patients in this country. By contrast, warnings in North America did not influence new antidepressant prescription rates in any patient group.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".