Antidepressant use among survivors of childhood, adolescent and young adult cancer: A report of the childhood, adolescent and young adult cancer survivor (CAYACS) research program
Bibliographic record
Abstract
BACKGROUND: Although survivors of childhood, adolescent, and young adult (AYA) cancer are at risk for late psychological sequelae, it is unclear if they are more likely to be prescription antidepressant users than their peers. PROCEDURE: All 5-year survivors of childhood or AYA cancer diagnosed before age 25 years in British Columbia from 1970 to 1995 were identified. Those with complete follow-up in the provincial health insurance registry from 2001 to 2004 were included (n = 2,389). A birth-cohort and gender-matched set of population controls 10 times the size of the survivor group was randomly selected (n = 23,890). All prescriptions filled between 2001 and 2004 were identified through linkage to the provincial prescription drug administrative database. Logistic regression analyses determined the impact of cancer survivorship on the likelihood of ever filling an antidepressant prescription. RESULTS: After adjusting for sociodemographic factors, survivors of childhood and AYA cancer were more likely to have filled an antidepressant prescription compared to controls (OR 1.21, 95% CI 1.09-1.35). Cancer survivors had an increased likelihood of using all categories of antidepressants, and of using drugs from two or more antidepressant categories, compared to peers (OR 1.31, 95% CI 1.11-1.55 [≥2 antidepressant categories]). Treatment was not a significant predictor of antidepressant use. Female survivors, those in young adulthood and those more than 20 years post-treatment had increased antidepressant use. CONCLUSIONS: Survivors of childhood and AYA cancer are more likely to fill antidepressant prescriptions compared to peer controls. This may indirectly reflect an increased underlying prevalence of mental health conditions among survivors.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".