Factors Predicting Subsequent Hospitalization in Patients with Ulcerative Colitis: Total Colonoscopic Findings are the Strongest Predictor.
Bibliographic record
Abstract
BACKGROUND/AIMS: Patients with ulcerative colitis suffer from long term impairment of quality of life, especially when subjected to repeated hospitalization. We aimed to identify factors that may predict future hospitalization. METHODOLOGY: We followed 139 consecutive patients with ulcerative colitis for average of 11.2 years (2.8 to 49.5 years) from the onset. Clinical and endoscopic stagings were determined by Japanese staging system, the extent of colitis by Montreal classification and endoscopic grading by Matts' grade. RESULTS: Overall hospitalization rate was 37% at 5 years, 47% at 10 years and 60% at 20 years from the onset. Of 5 parameters including demographic and staging scores, univariate analysis revealed clinical severity at onset (p = 0.003), total colonoscopic findings on severity (Matts' grade, p = 0.003), and total colonoscopic findings on sites of abnormality (p = 0.012) were significantly correlated with hospitalization. By multivariate analysis, total colonoscopic findings on sites of abnormality was the only baseline character significantly related to the need of hospitalization (p = 0.0007). In fact, 5/10/20 years hospitalization rates were only 18/26/33 percent for proctitis type, whereas those were 61/72/90 for total colitis type. CONCLUSIONS: The total colonoscopic finding on sites of abnormality at the onset is the only predictdr of hospitalization in patients with 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".