Maternal and neonatal factors associated with perinatal deaths in a South African healthcare institution
Bibliographic record
Abstract
Background: Research indicated the prevalence of perinatal deaths of infants immediately or up to a week after birth and includes fresh and macerated stillbirths and neonatal deaths. Worldwide, there is a decline in perinatal deaths. However, in South Africa, it is not the case. Often the quality of maternity care is considered as the most important contributing factor for these deaths. However, maternal and neonatal factors can also contribute. Aim: The aim of the study was to determine the maternal and neonatal factors associated with perinatal deaths in a single selected district hospital within the Free State Province of South Africa. Setting: The maternity unit of the largest district hospital in the specific district in the Free State Province of South Africa. Method: A clinical audit design was used. Units of analysis comprised the Perinatal Problem Identification Programme (PPIP) database of neonates born during 2015, and their mothers. A random sample of 384 alive neonates and an all-inclusive sample of 43 deceased neonates were taken from a total of 2319. Descriptive statistics were reported and Cohen’s effect sizes, d , were calculated to identify practically significant differences between the neonates in the alive and the deceased group, respectively. Results: Cohen’s effect sizes and logistical regression analyses indicate that the Apgar score recorded 10 min after birth, gestational age, birth weight of neonate and the parity of the mother were the most practically significant factors influencing a neonate’s chances of survival. Conclusion: Quality maternity care is not the only cause of perinatal mortality rates; maternal and neonatal factors are also contributors.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".