Exploring the reliability of phenotypic assignment for pediatric inflammatory bowel disease.
Bibliographic record
Abstract
The Montreal Classification1 was developed to provide a uniform system of designating subgroups of patients with inflammatory bowel disease (IBD), with the aim of facilitating multi-center genotype-phenotype correlation studies. Although the classification is frequently used for pediatric IBD patients, its reliability in this population has never been evaluated. To determine the reproducibility with which experienced IBD clinical researchers apply the Montreal Classification in pediatric settings. Case scenarios were constructed using de-identified data from the medical records of 50, randomly-selected, pediatric IBD patients. Data pertaining to clinical presentation, radiologic and endoscopic investigations were recorded. International IBD experts classified these cases using the Montreal Classification on two separate occasions. Inter-rater and intra-rater reliability in the assignment of an overall diagnosis of Crohn's disease (CD), ulcerative colitis (UC) or inflammatory bowel disease - type undefined (IBDU) were determined using the kappa statistic. The single kappa statistic for inter-rater reliability, which summarizes the level of agreement amongst all raters, was calculated using a SAS® Macro2. The kappa statistic for intra-rater reliability was determined for each rater individually using standard SAS® coding. A kappa value of > 0.6 and > 0.8 was considered to indicate good (or moderate) and excellent reliability respectively.
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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 | 0.073 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".