A novel location classification system for Crohn’s disease based on small bowel involvement: a better predictor of disease progression
Bibliographic record
Abstract
Abstract Background Small bowel involvement is related to poor prognosis in Crohn’s disease (CD), which may be a potential marker to stratify patients with a high risk of progression. This study aimed to establish a novel location classification system for CD and to develop a predictive model for disease progression. Methods Consecutive patients with non-stricturing/non-penetrating CD were retrospectively included in the Sixth Affiliated Hospital, Sun Yat-sen University (Guangzhou, P. R. China) between January 2012 and January 2018. Patients were classified into two groups according to disease location: small bowel involvement group and isolated colon group. The primary outcome was disease progression to stricturing or penetrating phenotypes. Progression-free survival was estimated using Cox proportional hazards regression analysis and Kaplan–Meier method. Results A total of 463 patients were analysed, with a median follow-up time of 55.3 months. Patients with small bowel involvement had a higher risk of disease progression than those with isolated colon disease (hazard ratio = 1.998, P = 0.007), while no differences were found between Montreal location classification and disease progression. Median progression-free survival was higher in the isolated colon group than in the small bowel involvement group (84.5 vs 77.3 months, P = 0.006). Four independent factors associated with disease progression were identified: small bowel involvement, duration of onset of >1 year, deep mucosal ulcer, and C-reactive protein levels of ≥10 mg/L (all P < 0.05). The nomogram model based on these factors showed good performance in predicting disease progression, with a C-index of 0.746 (95% confidence interval, 0.707–0.785). Conclusions Classifying CD based on small bowel involvement and isolated colon was superior to the Montreal location classification for predicting disease progression.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".