Predicting outcomes in congenital diaphragmatic hernia
Bibliographic record
Abstract
Identification of CDH infant populations at high risk for mortality postnatally may help to develop targeted care strategies, guide discussions surrounding palliation and contribute to standardizing reporting and benchmarking, so that care strategies at different centers can be compared. Clinical prediction rules are evidence-based tools that combine multiple predictors to estimate the probability that a particular outcome in an individual patient will occur. In CDH, a suitable clinical prediction rule can stratify high- and low-risk populations and provide the ability to tailor management strategies based on severity. The ideal prediction tool for infants born with CDH would be validated in a large population, generalizable, easily applied in a clinical setting and would clearly discriminate patients at the highest and lowest risk of death. To date, 4 postnatal major clinical prediction rules have been published and validated in the North American CDH population. These models contain variables such as birth weight, Apgar score, blood gases, as well as measures of pulmonary hypertension, and associated anomalies. In an era of standardized care plans and population-based strategies, the appropriate selection and application of a generalizable tool to provide an opportunity for benchmarking, policy creation, and centralizing the care of high-risk populations. A well-designed clinical prediction tool remains the most practical and expedient way to achieve these goals.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".