Lack of Utility of Symptoms and Signs at First Presentation as Predictors of Inflammatory Bowel Disease in Secondary Care
Bibliographic record
Abstract
OBJECTIVES: There are few data concerning the utility of symptoms and signs at first presentation in predicting a diagnosis of ulcerative colitis (UC) or Crohn's disease (CD). We conducted a study to examine this issue in secondary care. METHODS: We collected complete symptom, colonoscopy, and histology data prospectively from 1,981 consecutive adult patients with lower gastrointestinal symptoms at two hospitals in Hamilton, Ontario. Assessors were blinded to symptom status. The reference standard used to define the presence of UC or CD was according to accepted histological criteria. Patients without UC or CD served as controls. Sensitivity, specificity, and positive and negative likelihood ratios (LRs) were calculated for individual items from the clinical history, as well as combinations of these. RESULTS: In identifying 302 patients with inflammatory bowel diseases (IBD), positive LRs for individual items ranged from 1.18 (incomplete emptying) to 2.30 (passage of stools more than four times per day at least most of the time) and negative LRs from 0.70 (bloody stools) to 0.96 (incomplete emptying). Combinations of items had a high specificity, but at the expense of sensitivity. Items that were independent predictors of IBD after logistic regression analysis were family history of IBD, younger age, passage of stools more than four times per day ≥75% of the time, urgency most of the time, and anemia. CONCLUSIONS: Individual items from the clinical history are not helpful in predicting a diagnosis of UC or CD. However, this may be because some items lacked sufficient detail. Combinations of symptoms and computer models had a high specificity, but overall were only modestly useful diagnostically. Future studies should evaluate biological markers in combination with symptoms to improve accuracy.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".