Dyssynergic Defecation in Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Inflammatory bowel disease (IBD) patients often continue to experience nonspecific gastrointestinal symptoms despite quiescent disease. Unlike non-IBD patients, IBD patients with dyssynergic defecation (DD) may present with various symptoms such as diarrhea, fecal incontinence, constipation, and rectal discomfort. Despite its importance and treatability, DD in IBD patients is not well recognized in practice. We conducted a systematic review and meta-analysis on the prevalence, diagnosis, and management of DD in IBD patients with ongoing defecatory symptoms. Methods: We searched MEDLINE, EMBASE, and Cochrane Database of Systematic Reviews (from 1966 through February 2017) to identify relevant studies on the prevalence, diagnostic methods, or management of DD in IBD patients with and without ileal pouch-anal anastomoses (IPAAs). A random effects model was used to calculate the pooled estimates with 95% confidence intervals (CIs). Heterogeneity was assessed with I2 statistics, Cochran Q statistic, and sensitivity analyses. Results: Seven studies (n = 442) were included. In patients with ongoing defecatory symptoms, the prevalence of DD without IPAA ranged from 45% to 97%, and in patients with IPAA, it ranged from 25% to 75%. The prevalence of DD in IPAA patients with and without pouchitis ranged from 17% to 67% and 29% to 50%, respectively. The pooled response rate to biofeedback therapy in patients without IPAA was 70% (95% CI, 55%-84%; I2 = 95%; P < 0.01), and it was 86% (95% CI, 67%-98%; I2 = 61%; P = 0.05) in those with IPAA. Conclusions: Despite limited data, the current literature suggests that DD is highly prevalent in active or quiescent IBD patients with ongoing defecatory symptoms and is responsive to biofeedback therapy. Although more studies are needed, DD should be considered in IBD patients with persistent defecatory symptoms.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.006 | 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".