Prediction of outcome within the first four hours after birth of a term infant with post-asphyxial hypoxic ischemic encephalopathy
Bibliographic record
Abstract
Prediction of long-term neurodevelopmental outcome following post-asphyxial hypoxic ischemic encephalopathy can assist in the selection of patients for neuroprotective therapy trials and counseling families. A logistic regression model was developed from available clinical and biochemical data within the first 4 hours after birth. Of 375 patients, 345 had follow-up data. Six statistically significant independent predictors for "severe adverse outcome" were identified: administration of chest compression for>1 minute, administration of epinephrine, age of first respiration ge;30 minutes, presence of seizures/coma before 4 hours of age, standardized base deficit at the age of sampling, and birth following cesarean section. The prediction success rate of the model was 73.2%. For "severe adverse outcome" the sensitivity, specificity, positive predictive value, and negative predictive value were 89%, 41%, 76% and 63% respectively. Model diagnostics, residual analysis and internal validation revealed that the model was robust. Further research involves an external validation of this model.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".