P120 Prevalence of endoscopic improvement and endoscopic remission according to patient reported outcomes in ulcerative colitis
Bibliographic record
Abstract
Abstract Background We aimed to quantify the prevalence of endoscopic improvement (EI) and remission (ER) amongst ulcerative colitis patients with various permutations of patient reported outcomes (PRO) following treatment with biologic agents or tofacitinib. Methods Individual participant data from active intervention and placebo arms of clinical trials of infliximab, golimumab, vedolizumab, and tofacitinib were pooled to estimate the prevalence of EI (Mayo endoscopic sub-score [MES] 0 or 1) and ER (MES 0) scores with various permutations of the rectal bleeding sub-score (RBS) and stool frequency sub-score (SFS) of the Mayo score, following induction (6–8 weeks) and maintenance (30–54 weeks) therapy. Subgroup analyses were performed by year of publication and centrally read endoscopy scoring. Results Data from 2586 trial participants were analysed. Using locally scored endoscopy, the prevalence of EI and ER was highest among participants with a RBS 0 + SFS 0 post induction (EI: 81%, [95% CI 78–84]; ER: 29% [26–33]) and during maintenance (EI: 91% [87–93]; ER: 57% [52–62]). Prevalence estimates were lower for more recently performed trials (p < 0.01). In comparison to locally scored endoscopy, when using central endoscopy scoring the prevalence of EI and ER were lower post-induction (EI 57% [50–64], p < 0.001; ER 15% [11–21], p = 0.09) and during maintenance (EI 74% [67–81], p = 0.001; ER 31% [24–38], p = 0.001) for participants achieving a RBS 0 + SFS 0. Conclusion Approximately eight out of 10 patients with normalisation of rectal bleeding and stool frequency have improvement in endoscopic disease activity, whereas approximately only half of these patients have endoscopic remission.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".