Association between antidepressant medication use and steroid dependency in patients with ulcerative colitis: a population-based study
Bibliographic record
Abstract
Background Animal studies indicate a potential protective role of antidepressant medication (ADM) in models of colitis but the effect of their use in humans with ulcerative colitis (UC) remains unclear. Objective To study the relationship between ADM use and corticosteroid dependency in UC. Design Using the Clinical Practice Research Datalink we identified patients diagnosed with UC between 2005 and 2016. We grouped patients according to serotonin selective reuptake inhibitor (SSRI) and tricyclic antidepressant (TCA) exposure in the 3 years following diagnosis: ‘continuous users’, ‘intermittent users’ and ‘non-users’. We used logistic regression to estimate the adjusted risk of corticosteroid dependency between ADM exposure groups. Results We identified 6373 patients with UC. Five thousand two hundred and thirty (82%) use no ADMs, 627 (10%) were intermittent SSRI users and 282 (4%) were continuous SSRI users, 246 (4%) were intermittent TCA users and 63 (1%) were continuous TCA users. Corticosteroid dependency was more frequent in continuous SSRI and TCA users compared with non-users (19% vs 24% vs 14%, respectively, χ2p=0.002). Intermittent SSRI and TCA users had similar risks of developing corticosteroid dependency to non-users (SSRI: OR 1.19, 95% CI 0.95 to 1.50, TCA: OR 1.14, 95% CI 0.78 to 1.66). Continuous users of both SSRIs and TCAs had significantly higher risks of corticosteroid dependency compared with non-users (SSRI: OR 1.62, 95% CI 1.15 to 2.27, TCA: OR 2.02, 95% CI 1.07 to 3.81). Conclusions Continuous ADM exposure has no protective effect in routine clinical practice in UC and identifies a population of patients requiring more intensive medical therapy. ADM use is a flag for potentially worse clinical outcomes in UC.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".