An international multicenter validation study of the Toronto listing criteria for pediatric intestinal transplantation
Bibliographic record
Abstract
Deciding which patients would benefit from intestinal transplantation (IT) remains an ethical/clinical dilemma. New criteria* were proposed in 2015: ≥2 intensive care unit (ICU) admissions, loss of ≥3 central venous catheter (CVC) sites, and persistently elevated conjugated bilirubin (CB ≥ 75 μmol/L) despite 6 weeks of lipid modification strategies. We performed a retrospective, international, multicenter validation study of 443 children (61% male, median gestational age 34 weeks [IQR 29-37]), diagnosed with IF between 2010 and 2015. Primary outcome measure was death or IT. Sensitivity, specificity, NPV, PPV, and probability of death/transplant (OR, 95% confidence intervals) were calculated for each criterion. Median age at IF diagnosis was 0.1 years (IQR 0.03-0.14) with median follow-up of 3.8 years (IQR 2.3-5.3). Forty of 443 (9%) patients died, 53 of 443 (12%) were transplanted; 11 died posttransplant. The validated criteria had a high predictive value of death/IT; ≥2 ICU admissions (p < .0001, OR 10.2, 95% CI 4.0-25.6), persistent CB ≥ 75 μmol/L (p < .0001, OR 8.2, 95% CI 4.8-13.9). and loss of ≥3 CVC sites (p = .0003, OR 5.7, 95% CI 2.2-14.7). This large, multicenter, international study in a contemporary cohort confirms the validity of the Toronto criteria. These validated criteria should guide listing decisions in pediatric IT.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".