The Modified Mayo Endoscopic Score (MMES): A New Index for the Assessment of Extension and Severity of Endoscopic Activity in Ulcerative Colitis Patients
Bibliographic record
Abstract
BACKGROUND AND AIMS: Current endoscopic activity scores for ulcerative colitis (UC) do not take into account the extent of mucosal inflammation. We have developed a simple endoscopic index for UC that takes into account the severity and distribution of mucosal inflammation. METHODS: In this multicentre trial, UC patients undergoing colonoscopy were prospectively enrolled. For the Modified Score (MS), the sum of Mayo Endoscopic Subscores (MESs) for five colon segments (ascending, transverse, descending, sigmoid and rectum) was calculated. The Extended Modified Score (EMS) was obtained by multiplying the MS by the maximal extent of inflammation. The Modified Mayo Endoscopic Score (MMES) was obtained by dividing the EMS by the number of segments with active inflammation. Colon biopsies were obtained from the rectum and sigmoid, as well as from all inflamed segments, by standard methods. Clinical activity was scored according to the Partial Mayo Score (PMS). Biological activity was scored according to C-reactive protein (CRP) and faecal calprotectin (FC) levels. Histological activity was scored according to the Geboes Score (GS). RESULTS: One hundred and seventy-one UC patients (38% female, median age 47 years, median disease duration 13 years) were included. The MMES correlated significantly with the PMS (r = 0.535), CRP (r = 0.238), FC (r = 0.730) and GS (r = 0.615) (all p < 0.001). Median MMES scores were significantly higher in patients with clinical, biological or histological activity (all p ≤ 0.001) CONCLUSIONS: The MMES is an easy to use endoscopic index for UC that combines the severity analysis of the MES with disease extent, and correlates very well with clinical, biological and histological disease activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".