Trends in antidepressant prescribing to children and adolescents in <scp>Canadian</scp> primary care: <scp>A</scp> time‐series analysis
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to describe the trends and patterns of antidepressant (AD) prescribing to children and adolescents in Canadian primary care before and after the black-box warning in 2004. METHODS: Prescription data from the Canadian Primary Care Sentinel Surveillance Network, a repository of primary care data on over 1 million patients, was used to analyze AD prescribing to children (8-11 y) and adolescents (12-18 y) between 2000 and 2014. Interrupted time series analyses were used to assess the impact of the 2004 black-box warning on the prescribing levels of ADs. RESULTS: The 2004 black-box warning had a significant and immediate effect on the prescribing of AD. However, this drop was not sustained, and 5 years after the advisory AD prescribing rates reversed direction and started to rise. Selective serotonin reuptake inhibitors dominated as the most common AD prescribed throughout the study period, increasing from 66% prior to the black-box warning to 83.12% after 2009. CONCLUSIONS: The black-box warning effectively reduced AD prescribing in primary care for approximately 5 years before a reversal back to a positive rate of prescribing. This rebounding could reflect an emerging consensus about the trade-off in risks and benefits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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".