Validation of a Prediction Rule for Mortality in Congenital Diaphragmatic Hernia
Bibliographic record
Abstract
BACKGROUND: Congenital diaphragmatic hernia (CDH) is a rare congenital anomaly with a mortality of ∼27%. The Congenital Diaphragmatic Hernia Study Group (CDHSG) developed a simple postnatal clinical prediction rule to predict mortality in newborns with CDH. Our aim for this study is to externally validate the CDHSG rule in the European population and to improve its prediction of mortality by adding prenatal variables. METHODS: We performed a European multicenter retrospective cohort study and included all newborns diagnosed with unilateral CDH who were born between 2008 and 2015. Newborns born from November 2011 onward were included for the external validation of the rule (n = 343). To improve the prediction rule, we included all patients born between 2008 and 2015 (n = 620) with prenatally diagnosed CDH and collected pre- and postnatal variables. We build a logistic regression model and performed bootstrap resampling and computed calibration plots. RESULTS: With our validation data set, the CDHSG rule had an area under the curve of 79.0%, revealing a fair predictive performance. For the new prediction rule, prenatal herniation of the liver was added, and absent 5-minute Apgar score was taken out. The new prediction rule revealed good calibration, and with an area under the curve of 84.6%, it had good discriminative abilities. CONCLUSIONS: In this study, we externally validated the CDHSG rule for the European population, which revealed fair predictive performance. The modified rule, with prenatal liver herniation as an additional variable, appears to further improve the model’s ability to predict mortality in a population of patients with prenatally diagnosed CDH.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".