Predictors of disease severity in ulcerative colitis patients from Southwestern Ontario
Bibliographic record
Abstract
AIM: To understand the demographic characteristics of patients in Southwestern Ontario, Canada with ulcerative colitis (UC) in order to predict disease severity. METHODS: Records from 1996 to 2001 were examined to create a database of UC patients seen in the London Health Sciences Centre South Street Hospital Inflammatory Bowel Disease Clinic. To be included, patients' charts were required to have information of their disease presentation and a minimum of 5 years of follow-up. Charts were reviewed using standardized data collection forms. Disease severity was generated during the chart review process, and non-endoscopic Mayo Score criteria were collected into a composite. RESULTS: One hundred and two consecutive patients' data were entered into the database. Demographic analyses revealed that 51% of the patients were male, the mean age at diagnosis was 39 years, 13.7% had a first degree relative with inflammatory bowel disease (IBD), 61.8% were nonsmokers and 24.5% were ex-smokers. In 22.5% of patients the disease was limited to the rectum, in 21.6% disease was limited to the sigmoid colon, in 22.5% disease was limited to the left colon, and 32.4% of patients had pancolitis. Standard multiple regression analysis which regressed a composite of physician global assessment of disease severity, average number of bowel movements, and average amount of blood in bowel movements on year of diagnosis and age at time of diagnosis was significant, R(2) = 0.306, F (7, 74) = 4.66, P < 0.01. Delay from symptoms to diagnosis of UC, gender, family history of IBD, smoking status and disease severity at the time of diagnosis did not significantly predict the composite measure. CONCLUSION: UC severity is associated with younger age at diagnosis and year of diagnosis in a longitudinal cohort of UC patients, and may identify prognostic UC indicators.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".