Factors Predictive of Proximal Disease Extension and Clinical Course of Patients Initially Diagnosed with Ulcerative Proctitis in an IBD Referral Center
Bibliographic record
Abstract
BACKGROUND: This study aims to determine whether risk factors at the time of diagnosis that are found to be predictive of proximal dis- ease extension in ulcerative proctitis (UP) occur in a cohort of Brazilian patients. METHODS: This is a retrospective analysis of data from 97 patients (67% female) with UP (Montreal classification: E1) with at least 12 months of follow-up who were admitted to the Ribeirão Preto Medical School IBD referral center between January 2001 and December 2018. Proximal disease extension, which was defined as E1 progressing to E3 (pancolitis), was evaluated endoscopically during follow-up. RESULTS: A total of 29 (29.9%) patients experienced proximal disease extension. The risk factors at diagnosis associated with proximal disease extension were younger age (<40 years; P = .012), higher Mayo endoscopic score (P < .0001), higher partial Mayo score (P = .0018), and use of oral corticosteroids (P = .0016). During the follow-up period, increased disease relapse rates (P < .0001), immuno- modulators (P = .00014) or the use of biological agents (P = .00037), and colectomy (P = .0002) were all significantly higher among UP patients with proximal disease extension. CONCLUSION: Similar to what has been demonstrated in other studies, Brazilian UP patients with increased clinical and endoscopic sever- ity at the time of diagnosis are likely to evolve with both proximal extension and a more adverse clinical course. Therefore, these patients should be followed-up more carefully.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".