Antidepressant treatment in inflammatory bowel disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Around 25% of patients with inflammatory bowel disease (IBD) have depressive symptoms, yet antidepressants have been poorly studied in IBD. We systematically searched IBD studies testing antidepressants in four databases. Outcomes were depressive symptoms, anxiety, IBD disease activity, quality of life (QoL) and adverse events. For randomized controlled trials (RCTs), we performed random-effects meta-analysis of the standardized mean difference (SMD) in posttreatment scores between antidepressant and placebo groups. Risk of bias was assessed using the Cochrane Common Mental Disorders Depression Anxiety and Neurosis Group tool (clinical trials) and Newcastle-Ottawa scale (cohort studies). We included 11 studies ( n = 327): three placebo-controlled RCTs, two nonrandomized trials, and six other study types. In the pooled analysis, antidepressants improved depressive symptoms [SMD = -0.71 (95% confidence interval (CI) -1.32 to -0.10), P = 0.02, I2 = 51%] and QoL [SMD = 0.88 (95% CI 0.30-1.45), P = 0.003, I2 = 44%] more than placebo. Serotonin and noradrenaline reuptake inhibitors (SNRIs) alone improved depressive symptoms [SMD = -0.95 (95% CI -1.45 to -0.45, P < 0.001, I2 = 11%], anxiety [SMD = -0.92 (95% CI 1.72 to -0.13), P = 0.023, I2 = 65%] and QoL [SMD = 1.14 (95% CI 0.66-1.62), P < 0.001, I2 = 0%]. The three RCTs were of good quality. In conclusion, based on three small but good-quality studies, antidepressants improve depressive symptoms and QoL compared to placebo in IBD. SNRI antidepressants may also improve anxiety. A fully powered study of antidepressants in IBD is needed.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".