Supplementary Material for: Outcomes of Neonates with a 10-min Apgar Score of Zero: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: The Apgar score is a standardized method of assessing the primary adaptation and clinical status of a neonate after birth. Our objective was to systematically review and meta-analyze the survival and the survival without moderate-to-severe neurodevelopmental impairment (NDI) of neonates with a 10-min Apgar score of zero. Methods: Six electronic databases were searched for reports published until November 2021 of neonates with a 10-min Apgar score of zero. Risk of bias was assessed using the Newcastle-Ottawa scale for cohort studies and the Joanna Briggs Institute Critical Appraisal Checklist for case series/reports. Meta-analyses of the proportion of outcomes were conducted using a random-effects model for studies published after year 2000 and reporting >5 neonates. Meta-regression using the median year of the study period and subgroup analyses by treatment with therapeutic hypothermia and by gestational age were conducted. Results: Twenty-eight studies of 820 neonates with moderate risk of bias were included. Survival was 40% (95% confidence interval 30–50%, 16 studies, 646 neonates, I2 = 83%), and it increased by 2.3% per year (95% CI 1.3–3.2%, p < 0.001). Survival without moderate-to-severe NDI was 19% (95% confidence interval 11–27%, 13 studies, 211 neonates, I2 = 62%). Survival was higher for neonates who received therapeutic hypothermia and for those with a gestational age ≥32 weeks compared to <32 weeks. Conclusion: Approximately 2 in 5 neonates with a 10-min Apgar score of zero survived, and 1 in 5 survive without moderate-to-severe NDI survived. Survival has improved over the years, especially since the era of therapeutic hypothermia.
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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 | 0.004 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.638 | 0.028 |
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".