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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.023 | 0.003 |
| 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) | 0.003 | 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".