Predictors of Outcome in Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND: Approximately 80% of patients with ulcerative colitis (UC) have intermittently active disease and up to 20% will require a colectomy, but little data available on predictors of poor disease course. The aim of this study was to identify clinical and genetic markers that can predict prognosis. METHODS: Medical records of patients with UC with ≥5 years of follow-up and available DNA and serum were retrospectively assessed. Immunochip was used to genotype loci associated with immune mediated inflammatory disorders (IMIDs), inflammatory bowel diseases, and other single nucleotide polypmorphisms previously associated with disease severity. Serum levels of pANCA, ASCA, CBir1, and OmpC were also evaluated. Requirement for colectomy, medication, and hospitalization were used to group patients into 3 prognostic groups. RESULTS: Six hundred one patients with UC were classified as mild (n = 78), moderate (n = 273), or severe disease (n = 250). Proximal disease location frequencies at diagnosis were 13%, 21%, and 30% for mild, moderate, and severe UC, respectively (P = 0.001). Disease severity was associated with greater proximal extension rates on follow-up (P < 0.0001) and with shorter time to extension (P = 0.03) and to prednisone initiation (P = 0.0004). When comparing severe UC with mild and moderate UC together, diagnosis age >40 and proximal disease location were associated with severe UC (odds ratios = 1.94 and 2.12, respectively). None of the single nucleotide polypmorphisms or serum markers tested was associated with severe UC, proximal disease extension or colectomy. CONCLUSIONS: Older age and proximal disease location at diagnosis, but not genetic and serum markers, were associated with a more severe course. Further work is required to identify biomarkers that will predict outcomes in UC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".