Five-minute Apgar score and outcomes in neonates of 24–28 weeks’ gestation
Bibliographic record
Abstract
OBJECTIVES: To assess associations between 5 min Apgar score and mortality and severe neurological injury (SNI) and to report test characteristics in preterm neonates. DESIGN, SETTING AND PATIENTS: weeks' gestation born between 2007 and 2016 and admitted to neonatal units in 11 high-income countries. EXPOSURE: 5 min Apgar score. MAIN OUTCOME MEASURES: In-hospital mortality and SNI defined as grade 3 or 4 periventricular/intraventricular haemorrhage or periventricular leukomalacia. Outcome rates were calculated for each Apgar score and compared after adjustment. The diagnostic characteristics and ORs for each value from 0 versus 1-10 to 0-9 versus 10, with 1-point increments were calculated. RESULTS: Among 92 412 included neonates, as 5 min Apgar score increased from 0 to 10, mortality decreased from 60% to 8%. However, no clear increasing or decreasing pattern was identified for SNI. There was an increase in sensitivity and decrease in specificity for both mortality and SNI associated with increasing scores. The Apgar score alone had an area under the curve of 0.64 for predicting mortality, which increased to 0.73 with the addition of gestational age. CONCLUSIONS: In neonates of 24-28 weeks' gestation admitted to neonatal units, higher 5 min Apgar score was associated with lower mortality in a graded manner, while the association with SNI remained relatively constant at all scores. Among survivors, low Apgar scores did not predict SNI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".