Clinical Predictors of the Risk of Early Colectomy in Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND: A subset of patients with ulcerative colitis (UC) will require colectomy within a few years of diagnosis. Thus, our aim was to determine the clinical predictors of early colectomy among patients with UC who are hospitalized with an acute flare. METHODS: Using population-based surveillance (1996-2009), all adults (≥18 years) hospitalized for UC within 3 years of diagnosis (n = 489) were identified. The primary outcome was a colectomy within 3 years of diagnosis. All medical charts were reviewed. A logistic regression model evaluated clinical variables that predicted colectomy within 3 years of diagnosis, and adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were reported. RESULTS: Among patients admitted to hospital with UC within 3 years of diagnosis, 57.7% underwent colectomy, with the odds of colectomy decreasing by 12% per year. Early colectomy was more likely among patients aged 35 to 64 years versus 18 to 34 years (OR 2.18 [95% CI, 1.27-3.74]), males (OR 2.03 [95% CI, 1.24-3.34]), those with pancolitis (OR 5.38 [95% CI, 3.20-9.06]), and living in rural areas (OR 2.81 [95% CI, 1.49-5.29]). Prescription of infliximab before hospitalization increased odds of surgery (OR 5.12 [95% CI, 1.36-19.30]). CONCLUSIONS: Patients hospitalized for UC have a high risk of early colectomy. This is particularly true in middle-aged men, those living in rural areas, and those without response to infliximab.
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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.000 |
| 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".