Assessing the Validity of Adult‐derived Prognostic Models for Primary Sclerosing Cholangitis Outcomes in Children
Bibliographic record
Abstract
BACKGROUND: Natural history models for primary sclerosing cholangitis (PSC) are derived from adult patient data, but have never been validated in children. It is unclear how accurate such models are for children with PSC. METHODS: We utilized the pediatric PSC consortium database to assess the Revised Mayo Clinic, Amsterdam-Oxford, and Boberg models. We calculated the risk stratum and predicted survival for each patient within each model using patient data at PSC diagnosis, and compared it with observed survival. We evaluated model fit using the c-statistic. RESULTS: Model fit was good at 1 year (c-statistics 0.93, 0.87, 0.82) and fair at 10 years (0.78, 0.75, 0.69) in the Mayo, Boberg, and Amsterdam-Oxford models, respectively. The Mayo model correctly classified most children as low risk, whereas the Amsterdam-Oxford model incorrectly classified most as high risk. All of the models underestimated survival of patients classified as high risk. Albumin, bilirubin, AST, and platelets were most associated with outcomes. Autoimmune hepatitis was more prevalent in higher risk groups, and over-weighting of AST in these patients accounted for the observed versus predicted survival discrepancy. CONCLUSIONS: All 3 models offered good short-term discrimination of outcomes but only fair long-term discrimination. None of the models account for the high prevalence of features of autoimmune hepatitis overlap in children and the associated elevated aminotransferases. A pediatric-specific model is needed. AST, bilirubin, albumin, and platelets will be important predictors, but must be weighted to account for the unique features of PSC 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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".