P309 Accuracy of PanMayo endoscopic score in predicting long-term disease outcomes in ulcerative colitis– a promising scoring system
Bibliographic record
Abstract
Abstract Background Ulcerative colitis (UC) is a systemic immune-mediated disease that affects the colon continuously. Colonoscopy plays a crucial role in management of UC that helps to assess mucosal healing objectively along the colon. Different scoring systems are available to assess severity, however most of them does not take into consideration of disease extent. Extension modified (PanMayo) Mayo endoscopic subscore (MES) system has been shown to correlate with UCEIS and Riley scores and calprotectin. Our study aimed to assess the predictive power and accuracy of PanMayo score compared to MES, UCEIS and Dublin to predict mid- and long-term disease outcomes. Methods This is retrospective, two-center study. UC patients, who underwent colonoscopy due to any reason between 2016 and 2018, were consecutively enrolled. PanMayo, MES, UCEIS and Dublin scores and additionally the Nancy histology score (where available) were recorded with clinical and demographical data at baseline. Disease flare, need for change in therapy (incl. initiation of biologicals, need for systemic steroids), hospitalisations and colectomy were tracked amongst patients with clinical remission (pMayo<2) during an at least 3-years follow-up to assess predictive value of score systems. Log-rank, Cox regression analysis and Chi2 tests were used to analyse outcomes and Kaplan Meier curves were plotted. Results A total of 250 UC patients (male ratio: 0.45, median age 45 (IQR) 22.3 years) were enrolled with 156 (male ratio 0.49; mean age 46 IQR 20.8 years, Table 1.) of UC patients having baseline clinical remission. PanMayo, MES, and Dublin scores were positively associated with risk of disease flare (Figure 1.; p=0.002, p<0.01, p=0.003). Increasing MES score was coupled with risk of relapse (MES0=26.7%, MES1=53.1% and MES2-3=47.6%; p=0.008). PanMayo score (above 12 points), but not MES or UCEIS, was associated with the need of new biological (Figure 2.; 66.7% vs. 21.7%; p<0.001) and treatment escalation (33.3 vs. 11.5%; p=0.018), similar trend was found for the Dublin score for need for new biologicals. There was a strong trend for PanMayo to predict need for hospital admission (p=0.06). All scores were strongly associated with need for systemic corticosteroids. Colectomy rates were low. Nancy score showed only a trend to predict risk of clinical flare and need for corticosteroids. Conclusion Our study suggests that combined endoscopic assessment of the extent and severity may be more accurate in predicting disease outcomes in UC in clinical remission. PanMayo scores may be an alternative of the existing scoring system and was associated more granularly with disease outcomes. In addition, outcomes were different in patients with initial MES 0 and 1 scores.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".