Time to Endorse A Sensitive Method for Scoring Endoscopic Activity of Ulcerative Colitis in Clinical Research
Bibliographic record
Abstract
The endoscopic scoring of ulcerative colitis is routinely used for both individual patient management and as an endpoint in clinical trials. The most commonly used scoring system is the Mayo endoscopic subscore, which scores endoscopic disease activity on a scale between 0 and 3. With only four possible scores and consideration of only the most involved area of the colon, the Mayo endoscopic subscore lacks sensitivity to change in measuring the totality of the endoscopic inflammatory involvement in patients with ulcerative colitis. Here, we present one case study from clinical practice and one from clinical trials in which using the Mayo endoscopic score leads to potentially incorrect conclusions. Further, in a post-hoc analysis, we re-examined endoscopic videos from a clinical trial and demonstrate that assessing involved ulcerated and affected areas on a segmental level of the colon or summing Mayo scores of colonic segments can identify improvements in endoscopic disease activity in almost twice as many subjects as identified by the Mayo endoscopic subscore alone. Although the alternative scoring systems we have used in this report will need further validation, our findings demonstrate the need for a more sensitive endoscopic scoring system in ulcerative colitis.
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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 | 0.103 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".