Long-Term Trend in Pediatric Antidepressant Use, 1983–2007: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: Research is needed to clarify and improve our understanding of appropriateness and safety issues concerning antidepressant (AD) treatment. We explored the long-term trend in the dispensing of pediatric ADs using provincial, population-based data from Canada. METHODS: Data covering 22 ADs were drawn from the Saskatchewan Ministry of Health administrative data files in outpatient settings. The data were for 9 triennial years from 1983 to 2007, a 24-year period, for those aged 0 to 19 in the general population. Descriptive analyses were used. RESULTS: In 1983, 5.9 per 1000 population aged 0 to 19 were dispensed at least 1 AD; this decreased to 5.1 per 1000 population in 1989, and then increased to 15.4 per 1000 population in 2007, with a slower increase after 2004. Both sexes were dispensed more ADs from 1989 onwards, with females being the heavier users. The rate of AD use increased significantly with age, and this trend became more pronounced after 1998. Family physicians were the major prescribers and their prescriptions significantly increased from 1989 to 2004 and decreased in 2007. The use of selective serotonin reuptake inhibitors (SSRIs) was the major reason for the increase. The number of AD scripts per patient also increased. CONCLUSIONS: The growth in the prevalence of AD use among children and youth was largely caused by the use of SSRIs. The possibility of safety issues induced by AD use among children and adolescents, and different patterns of medication practice, suggest continuing education is warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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".