Appraisal of the PIBD-classes Criteria: A Multicentre Validation
Bibliographic record
Abstract
INTRODUCTION: The PIBD-classes criteria were developed to standardise the classification of children with inflammatory bowel disease [IBD], from Crohn's disease [CD], through IBD-unclassified [IBD-U], to typical ulcerative colitis [UC]. We aimed to further validate the criteria and to explore possible modifications. METHODS: This was a multicentre retrospective cohort study of children diagnosed with IBD with at least 1 year of follow-up. Clinical, radiological, endoscopic, and histological data were recorded at diagnosis and latest follow-up, as well as the 23 items of the PIBD-classes criteria. The PIBD-classes criteria were assessed for redundant items, and a simplified algorithm was proposed and validated on the original derivation cohort from which the PIBD-classes algorithm was derived. RESULTS: Of the 184 included children [age at diagnosis 13 ± 3 years, 55% males], 122 [66%] were diagnosed by the physician with CD, 17 [9%] with IBD-U, and 45 [25%] with UC. There was high agreement between physician-assigned and PIBD-classes generated diagnosis for CD [93%; eight patients moved to IBD-U] and for UC [84%; six moved to IBD-U and one to CD]. A simplified version of the algorithm with only 19 items is suggested, with comparable performance to the original algorithm [81% sensitivity and 81% specificity vs 78% and 83% for UC; and 79% and 95% vs 80% and 95% for CD, respectively]. CONCLUSIONS: The PIBD-classes algorithm is a useful tool to facilitate standardised objective classification of IBD subtypes in children. A modified version of the PIBD-classes maintains accuracy of classification with a simplified algorithm.
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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 | 0.049 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".