Dietary Diversity Score during Pregnancy is Associated with Neonatal Low Apgar Score: A Hospital-Based Cross-Sectional Study
Bibliographic record
Abstract
Background: Apgar score is an established index of neonatal well-being and development. Nutrition during pregnancy is an accepted risk factor for neonatal low Apgar score. Objective: To investigate the association between dietary diversity score and low Apgar score. Methods: This was a hospital based cross-sectional study. The study participants were 420 mothers who delivered and were attending the postnatal clinic at the Cape Coast Metropolitan Hospital. Mothers’ dietary information during pregnancy was assessed with a food frequency questionnaire. In reference to the FAOs women’s Dietary Diversity Score (DDS), the subjects were categorized into low, medium or high DDS. The primary outcome was Apgar score. Apgar scores < 5 were classified as low. Results: The mean age (± standard deviation, SD) of subjects was 26.7 ± 5.7 years with a range of 17 to 45 years. The prevalence of low Apgar score among the study population was 16.9%. Majority of the study participants had a low DDS in relation to low Apgar score whereas 7.5% had high DDS. After adjusting for potential confounding factors, the odds of low Apgar score in the low DDS group was three times higher than those who had high DDS (Adjusted odds ratio, AOR= 3.10, 95% confidence interval, CI=1.23-4.48). Conclusion: Dietary diversity score during pregnancy was associated with a low Apgar score in the study area. The results of this study reinforce the significance of adequate nutrition during pregnancy in the study area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".