Impact of Individual Colonic Segment Histological Activity on Disease Relapse in Patients with Ulcerative Colitis
Bibliographic record
Abstract
Background/Objectives: The aim of this study was to assess the role of histological activity in individual segments of the colon in predicting disease relapse in patients with ulcerative colitis. Methods: This was a prospective observational study on patients with ulcerative colitis in clinical remission. Biopsies were taken of multiple segments of the colon, and histological activity was assessed using the Geboes (GB) score. Patients were monitored for disease relapse for 12 months. The primary objective was to determine the predictive value of histological activity of the individual segments of the colon on disease relapse. The secondary objective was to assess whether having multiple segments in histological remission is associated with disease relapse. Results: Of 253 patients, 19% had disease relapse. Histological activity (GB ≥ 3.1) was not predictive of disease relapse for the rectum (adjusted odds ratio [aOR] 0.95, confidence interval [CI] 0.46–1.98, p = 0.894), sigmoid (aOR 0.67, CI 0.24–1.90, p = 0.451), descending colon (aOR 1.52, CI 0.43–5.39, p = 0.519), transverse colon (aOR 0.47, CI 0.10–2.18, p = 0.332), and right colon (aOR 1.75 CI 0.73–4.18, p = 0.209). Histological remission (GB ≤ 2.0) was also not predictive of remaining in remission for any individual colonic segment nor was there any benefit of having multiple segments with histological remission compared to having ≤1 segment in histological remission (aOR 0.56, CI 0.28–1.10, p = 0.093). Conclusions: Histological activity in any individual colonic segment or the number of colonic segments with histological remission was not predictive of disease relapse.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 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".