Appraisal of the Pediatric Crohn's Disease Activity Index on Four Prospectively Collected Datasets: Recommended Cutoff Values and Clinimetric Properties
Bibliographic record
Abstract
OBJECTIVES: The Pediatric Crohn's Disease Activity Index (PCDAI) is the outcome measure of choice in clinical trials of pediatric Crohn's disease. The aim of this study was to provide knowledge on its performance and accuracy of different cutoff scores. METHODS: Longitudinal data prospectively generated from four sources were used, including the REACH and budesonide trials, a North-American inflammatory bowel diseases (IBD) registry, and a cohort aimed at evaluating growth. Cutoff values of disease activity were determined by physician global assessment from the pooled cohort using serial receiver operator characteristic curves and area under the curve (AUC) as well as comparing the overall accuracy. Test-retest reliability and responsiveness were ascertained by comparing the baseline and follow-up scores, using an external anchor. RESULTS: A total of 437 children were included (268 (61%) males, mean age 12.9+/-2.6 years). To define remission, a composite definition of <10 points or <7.5 points without the height item had the highest accuracy; this addressed the limitation that height is not a responsive item. The best cutoff of 10-27.5 was determined for mild disease, 30-37.5 for moderate disease, 40-100 for severe disease, and a change of >12.5 points for response (AUC 0.8-0.9; P<0.001). Ninety children whose disease remained unchanged showed fair test-retest reliability (intraclass correlation coefficient=0.74-0.8; P<0.001). The PCDAI showed good responsiveness, as reflected from the correlational (r=0.7; P<0.001), distributional (Guyatt's responsiveness statistics=0.9), and diagnostic utility analysis (AUC 0.85 (95% confidence interval 0.81-0.88). CONCLUSIONS: The clinimetric properties of the PCDAI are sufficient to support its use in clinical research. Cutoff values suggested by this study differ slightly from those previously published on much smaller cohorts.
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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 | 0.089 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".