Accuracy of the Pancolonic Modified Mayo Score in predicting the long-term outcomes of ulcerative colitis: a promising scoring system
Bibliographic record
Abstract
Background: Different endoscopic scoring systems for assessing ulcerative colitis (UC) severity are available. However, most of them are not correlated with disease extent. Objectives: the endoscopic Mayo (MES), Ulcerative Colitis Endoscopic Index of Severity (UCEIS), and Dublin score in predicting long-term outcomes of UC. Design: This retrospective study enrolled consecutive UC patients who underwent colonoscopy before at least a 3-year follow-up. Methods: The PanMayo, MES, UCEIS, and Dublin scores and the baseline clinical and demographic characteristics of the participants were assessed. Endpoints were disease flare that required novel biological therapy, colectomy, and hospitalization. Patients were stratified using baseline clinical activity. Results: Approximately 62.8% of the 250 enrolled patients were in clinical remission. In these patients, the PanMayo, MES, and Dublin scores were positively associated with the risk of clinical flare. The MES score increased with clinical flare. The PanMayo score (>12 points), but not the MES score, was associated with the need for novel biological initiation and biological escalation. Furthermore, the Dublin and UCEIS scores of patients in remission who need novel biological treatment had a similar trend. Colectomy risk was associated with PanMayo and Dublin scores. Conclusion: The combined endoscopic assessment of disease extent and severity can be more accurate in predicting outcomes among patients with UC. PanMayo score can be utilized in addition to the existing scoring systems, thereby leading to a more accurate examination. Summary: UC endoscopic scores do not assess extension. Our study aimed to analyze the predictive value of the PanMayo score. Based on 250 patients, results showed that the long-term disease outcomes of UC could be predicted with the PanMayo score more accurately.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".