Systematic Review and Meta-analysis on the Incidence, Prevalence and Determinants of Discomfort in Inflammatory Bowel Disease
Bibliographic record
Abstract
Background: The symptom burden in inflammatory bowel disease (IBD) has a significant negative impact on the health-related quality of life (HRQOL). Patients with IBD report physical, psychological and social discomfort even during remission. Aim: To synthesize the best available evidence to determine the worldwide incidence, prevalence and determinants of discomfort in adults with inflammatory bowel disease (IBD). Methods: Following PRISMA recommendations, we searched the Medline, CINAHL, PsycInfo, Embase, Cochrane, Campbell and JBI Evidence Synthesis databases for studies on either incidence or prevalence of discomfort in English until January 2021. Data were extracted using the Joanna Briggs Institute's standardized extraction tools. Data that directly reported or could be used to calculate the incidence and prevalence of discomfort were extracted. Ten studies were eligible for inclusion in this review. Overall, the methodological quality of the included studies was considered moderate. Data measuring the incidence of discomfort in 6 out of 10 identified studies using the same measurement tool (EQ-5D) were pooled in a meta-analysis. Additional results have been presented in a narrative form, including tables. Results: There is no standardized definition or tool utilized to describe or measure discomfort in IBD. Synthesized findings demonstrate that discomfort is prevalent among adults living with IBD. Determinants of discomfort included health literacy, disease activity, hospitalization/surgery, age and gender, delayed diagnosis, local practice standards and quality of IBD care. Conclusions: More research is needed to identify the impact of discomfort on health-related outcomes for people with IBD and consequently appraise discomfort interventions for their efficacy.
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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 | 0.027 | 0.075 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.045 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".