Development and Validation of a Pediatric MRI-Based Perianal Crohn Disease (PEMPAC) Index—A Report from the ImageKids Study
Bibliographic record
Abstract
BACKGROUND: As part of the prospective multicenter ImageKids study, we aimed to develop and validate the pediatric MRI-based perianal Crohn disease (PEMPAC) index. METHODS: Children with Crohn disease with any clinical perianal findings underwent pelvic magnetic resonance imaging at 21 sites globally. The site radiologist and 2 central radiologists provided a radiologist global assessment (RGA) on a 100 mm visual analog scale and scored the items selected by a Delphi group of 35 international radiologists and a review of the literature. Two weighted multivariable statistical models were constructed against the RGA. RESULTS: Eighty children underwent 95 pelvic magnetic resonance imaging scans; 64 were used for derivation and 31 for validation. The following items were included: fistula number, location, length and T2 hyperintensity; abscesses; rectal wall involvement; and fistula branching. The last 2 items had negative beta scores and thus were excluded in a contending basic model. In the validation cohort, the full and the basic models had the same strong correlation with the RGA (r = 0.75; P < 0.01) and with the adult Van Assche index (VAI; r = 0.93 and 0.92; P < 0.001). The correlation of the VAI with the RGA was similar (r = 0.77; P < 0.01). The 2 models and the VAI had a similar ability to differentiate remission from active disease (area under the receiver operating characteristic curve, 0.91-0.94). The PEMPAC index had good responsiveness to change (area under the receiver operating characteristic curve, 0.89; 95% confidence interval, 0.69-1.00). CONCLUSIONS: Using a blended judgmental and mathematical approach, we developed and validated an index for quantifying the severity of perianal disease in children with CD. The adult VAI may also be used with confidence in children.
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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 | 0.017 | 0.035 |
| 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 | 0.001 |
| 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".