The association between antidepressant use and depression eight years later: A national cohort study
Bibliographic record
Abstract
Investigations of the effects of antidepressant treatment for individuals with major depression have focused on short-term outcomes in individuals that meet very specific criteria; however, there is limited knowledge about long-term outcomes associated with antidepressant use in general population samples. This study aimed to investigate the long-term outcomes associated with antidepressant use by focusing on 486 depressed adults in a prospective observational Canadian cohort in 1998/99. We used logistic regression to investigate the association between antidepressant use and depression status 8 years later. Non-random allocation to treatment was accounted for by a propensity-for-treatment model which included thirteen predictors of antidepressant use, including: severity of depressive symptoms, previous episodes of depression (from 1994 to 1997), physical health condition, social support and socio-demographic characteristics. 29% of individuals with major depression reported antidepressant use. After adjusting for propensity for treatment in 1998/99, and antidepressant use from 2000 to 2007, depressed individuals who reported antidepressant use in 1998/99 were less likely to be depressed in 2006/07 compared to those who did not report antidepressant use (OR = 0.36, 95% CI: 0.15-0.88). Amongst individuals with symptoms of major depression, those reporting use of anti-depressants at baseline exhibited improved long-term outcomes in comparison to those who did not report treatment.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".