The predictive ability of inflammatory biomarkers in patients with newly diagnosed Inflammatory Bowel Disease [IBD]: Analyses of preliminary pre‐ and post‐therapy data
Bibliographic record
Abstract
Predictors of disease course in IBD would be beneficial for early determination of patient risk and appropriate medical therapy. To evaluate their predictive performance, endoscopic appearance, fecal calprotectin (Cp) and serological high sensitivity C‐reactive protein (hsCRP), markers of inflammation, were assessed in a prospective IBD (Crohn's disease [CD] and ulcerative colitis [UC]) inception cohort (<12 mo of diagnosis). Patients were observed at baseline and subsequent clinical visits (3 or 6 mo), where detailed endoscopic, clinical disease activity, and biomarker measurements were assessed. Of the 24 patients enrolled in the study, 14 were diagnosed with CD and 10 with UC. At enrolment, the median hsCRP was 4.1 mg/L (normal 0–7.0), and Cp was 644 μg/g (<50 normal). After therapy, the median Cp was 194 and 149 ug/g in CD (n=7) and UC (n=6), respectively. When stratified by clinical disease activity, no meaningful correlation between hsCRP and Cp was apparent, in contrast to a positive correlation with endoscopic appearance. Although the sample size is small, preliminary data suggest that the use of clinical disease activity measures in newly diagnosed IBD may be inadequate for determining inflammatory burden and for risk stratification. (Support from the Royal University Hospital Saskatoon and NSERC)
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".