Development and validation of a blood routine-based extent and severity clinical decision support tool for ulcerative colitis
Bibliographic record
Abstract
Monitoring extent and severity is vital in the ulcerative colitis (UC) follow-up, however, current assessment is complex and low cost-effectiveness. We aimed to develop a routine blood-based clinical decision support tool, Jin's model, to investigate the extent and severity of UC. The multicentre retrospective cohort study recruited 975 adult UC inpatients and sub-grouped into training, internal validation and external validation set. Model was developed by logistics regression for the extent via Montreal classification and for the severity via Mayo score, Truelove and Witts score (TWS), Mayo endoscopic score (MES) and Degree of Ulcerative colitis Burden of Luminal Inflammation (DUBLIN) score. In Montreal classification, left-sided and extensive versus proctitis model achieved area under the receiver operating characteristic curve (AUROC) of 0.78 and 0.81 retrospectively. For severity, Mayo score model, TWS model, MES model and DUBLIN score model achieved an AUROC of 0.81, 0.70, 0.74 and 0.70 retrospectively. The models also were evaluated with satisfactory calibration and clinical unity. Jin's model was free with open access at http://jinmodel.com:3000/ . Jin's model is a noninvasive, convenient, and efficient approach to assess the extent and severity of UC.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".