Anti-TNF Therapies Promote a Proximal-to-Distal Healing Pattern in Moderate-to-Severe Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND: Ulcerative colitis (UC) is a chronic inflammatory disease of the colonic mucosa, extending proximally from the rectum. However, the segmental pattern of healing in UC remains unclear. Endoscopic improvement (EI), a key therapeutic endpoint, is typically assessed using the Mayo endoscopic score (MES), which scores the worst affected area and may miss partial/segmental healing. This study evaluates healing patterns in UC and compares conventional MES with a 3-segment MES approach for detecting treatment effects in clinical trials. METHODS: A post hoc analysis of HIBISCUS I/II and GARDENIA trials was conducted in UC patients with moderate-to-severe disease (MES >2 up to the descending colon). The primary outcome was the proportion of anti-tumor necrosis factor-treated participants achieving MES ≤1 in the descending colon, sigmoid colon, and rectum at week 10. Secondary outcomes included conventionally measured EI, segmental MES improvements, clinical response, and Patient-Reported Outcome 2 (PRO2) normalization. Outcomes were compared between adalimumab, infliximab, and placebo groups. RESULTS: Among 300 participants, 217 received infliximab or adalimumab, while 83 received placebo. Healing followed a proximal-to-distal pattern, with the highest EI in the descending colon and the lowest in the rectum. Infliximab-treated patients continued this trend at week 54. Anti-tumor necrosis factor therapy significantly improved EI vs placebo (42.9% vs 19.3%; P < .001). No segmental MES approach outperformed conventional MES for detecting treatment effects. Combined endpoints (MES ≤1 + PRO2 normalization) better captured therapeutic benefits than PRO2 alone (28.6% vs 13.3%; P = .006). CONCLUSIONS: UC healing follows a proximal-to-distal pattern. Conventional MES remains superior for detecting treatment effects over segmental MES. Further studies should explore alternative endoscopic scoring methodologies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".