Systematic review of interventions for depression and anxiety in persons with inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety are common in inflammatory bowel disease (IBD) and can affect disease outcomes, including quality of life and success of disease treatment. Successful management of psychiatric comorbidities may improve outcomes, though the effectiveness of existing treatments in IBD is unknown. METHODS: We searched multiple online databases from inception until March 25, 2015, without restrictions on language, date, or location of publication. We included controlled clinical trials conducted in persons with IBD and depression or anxiety. Two independent reviewers reviewed all abstracts and full-text articles and extracted information including trial and participant characteristics. We also assessed the risk of bias. RESULTS: Of 768 unique abstracts, we included one trial of pharmacological anxiety treatment in IBD (48 participants), which found an improvement in anxiety symptoms (p < 0.001). There was a high risk of bias in this trial. We found no controlled clinical trials on the treatment of depression in persons with IBD and depression and no controlled clinical trials reporting on psychological interventions for anxiety or depression in IBD. CONCLUSIONS: Only one trial examined an intervention for anxiety in adults with IBD and no trials studied depression in adults with IBD. The level of evidence is low because of the risk of bias and limited evidence.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".