A Narrative Systematic Review and Categorisation of Outcomes in Inflammatory Bowel Disease to Inform a Core Outcome Set for Real-world Evidence
Bibliographic record
Abstract
BACKGROUND: Heterogeneity exists in reported outcomes and outcome measurement instruments [OMI] from observational studies. A core outcome set [COS] for observational and real-world evidence [RWE] in inflammatory bowel disease [IBD] will facilitate pooling large datasets. This systematic review describes and classifies clinical and patient-reported outcomes, for COS development. METHODS: The systematic review of MEDLINE, EMBASE, and CINAHL databases identified observational studies published between 2000 and 2021 using the population exposure outcome [PEO] framework. Studies meeting eligibility criteria were included. After titles and abstracts screening, full-text articles were extracted by two independent reviewers. Primary and secondary outcomes with corresponding OMI were extracted and categorised in accordance with OMERACT Filter 2.1 framework. The frequency of outcomes and OMIs are described. RESULTS: From 5854 studies, 315 were included: 129 [41%] Crohn's disease [CD], 60 [19%] ulcerative colitis [UC], and 126 [40%] inflammatory bowel disease [IBD] studies with 600 552 participants. Totals of 1632 outcomes and 1929 OMI were extracted mainly from medical therapy [181; 72%], surgical [34; 11%], and endoscopic [6; 2%] studies. Clinical and medical therapy-related safety were frequent outcome domains recorded in 194 and 100 studies. Medical therapy-related adverse events [n = 74] and need for surgery [n = 71] were the commonest outcomes. The most frequently reported OMI were patient or event numbers [n = 914], Harvey-Bradshaw Index [n = 45], and Montreal classification [n = 42]. CONCLUSIONS: There is substantial variability in outcomes reporting and OMI types. Categorised outcomes and OMI from this review will inform a Delphi consensus on a COS for future RWE in IBD. Data collection standardisation may enhance the quality of RWE applied to decision-making.
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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 | 0.045 | 0.190 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.032 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".