Influence of time of birth in early neonatal mortality and morbidity: retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: A key target of the 2030 Sustainable Development Goals is to eliminate preventable deaths in newborns and children under 5. This study aimed to estimate the effect of time of birth on early neonatal mortality (ENM) and low Apgar scores at 5 min (LA5) in newborns. METHODS: A retrospective cohort study was conducted using vital statistics data on live births, maternal morbidity, congenital defects and perinatal mortality in Cauca-Colombia (2017-2021) excluding out-of-hospital, multiple and major defect cases. A directed acyclic graph was constructed to define the confounder adjustment set. Multivariable logistic, linear and propensity score models evaluated the effect of birth timing on neonatal outcomes, estimating crude and adjusted incidence rate ratios (IRRa). RESULTS: We assessed 65 182 live births, finding similar baseline characteristics for daytime and night-time births. ENM was 0.2% (95% CI 0.19% to 0.26%) at 7 days of follow-up, absolute mortality difference 0.1% (95% CI -0.01% to 0.12%). Night-time births increased the incidence of ENM in the primary analysis IRRa 1.27 (95% CI 0.90 to 1.82), in the secondary IRRa 1.45 (95% CI 0.94 to 2.20), and in the primary and secondary sensitivity analysis, respectively, IRRa 1.48 (95% CI 1.06 to 2.07) and 1.70 (95% CI 1.16 to 2.59). LA5 was present in 0.7% (95% CI 0.60% to 0.72%) of birth, with absolute LA5 difference 0.1% (95% CI -0.02% to 0.22%). Night-time births increased the incidence of LA5 in the primary analysis IRRa 1.31 (95% CI 1.00 to 1.49), in the secondary IRRa 1.44 (95% CI 1.13 to 1.83), and in the primary and secondary sensitivity analysis, respectively, IRRa 1.31 (95% CI 1.08 to 1.59) and IRRa 1.54 (95% CI 1.23 to 1.92). CONCLUSIONS: Birth at night-time is associated with worse neonatal outcomes, ENM and low Apgar scores in Colombia's diverse population, highlighting the need for optimised prenatal care, revised work schedules and improved referral systems in maternal health.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".