Population‐Based Case–Control Study of Antidepressants in Early and Average‐Age Onset Colorectal Cancer: The Impact of Exposure Window, Class, Dose, and Intensity
Bibliographic record
Abstract
BACKGROUND: Given inconsistent findings from previous epidemiologic studies on the association between antidepressant exposure and colorectal cancer (CRC), our study provides a rigorous investigation to clarify the temporality of this association, including early-age onset (EAO) and average-age onset (AAO) CRC. METHODS: We conducted a population-based case-control study using administrative health databases from British Columbia, Canada. We included CRC cases and controls, matched (1:10) on age, sex, and index date (i.e., CRC diagnosis date/matched date). Antidepressant exposures were ascertained by duration (i.e., varying windows from 15 to 1 year before CRC diagnosis), drug classes (tricyclic antidepressants (TCAs), selective serotonin reuptake inhibiters (SSRIs), other), cumulative dose and treatment intensity. We used multivariable conditional logistic regression models and interpreted odds ratios as relative risks. RESULTS: Among 10,171 CRC cases (688 EAO-CRC; 9483 AAO-CRC) and 90 928 controls, antidepressants exposure in the 15-year window was associated with a lower risk of CRC overall (adjusted relative risk [aRR] 0.84; 95% CI 0.80, 0.89), EAO-CRC (aRR 0.54; 95% CI 0.44, 0.66), and AAO-CRC (aRR 0.87; 95% CI 0.83, 0.92). Across narrowing exposure windows, associations persisted up to 7 years before CRC diagnosis, then weakened. Inverse associations were also observed for TCAs (aRR 0.83; 95% CI 0.77, 0.89) and SSRIs (aRR 0.86; 95% CI 0.81, 0.91) and CRC. Cumulative dose and treatment intensity showed no associations. CONCLUSIONS: Across all age groups, antidepressant exposure in the earlier exposure windows (15-7 years) was associated with a lower CRC risk, with the strongest effect at the 15-year window.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".