Diet diversity score might be associated with reproductive health in women and infant outcomes: a systematic review
Bibliographic record
Abstract
Lifestyle and diet may affect the reproductive cycle. A dietary index called Diet Diversity Score (DDS) may be related to various reproductive outcomes. The present review aims to look over and conclude the prior studies on the relationship between the diversity of food ingredients and issues related to reproductive health and pregnancy. In the case of this relationship, our findings can increase clinical knowledge and help recommend a well-balanced diet for the target group. A comprehensive search was performed in major databases such as PubMed, Google Scholar, Web of Science, Scopus, and Scientific Information Database until March 2024. This research was combined with a search of Elsevier and SpringerLink databases, which led to the inclusion of relevant articles in this review. Our study was conducted based on 27 articles from 2012 to 2023, all containing a possible link between dietary diversity and reproductive complications. The Newcastle-Ottawa Scale quality assessment was used to evaluate the quality of included studies. Due to our results, a higher score in DDS, which led to an increased intake of major nutrients and a greater variety of foods, was correlated with a lower risk of reproductive health disorders such as polycystic ovary syndrome, maternal anaemia, and maternal bone status, as well as a reduced likelihood of certain birth outcomes, including low-birth weight infants, Apgar score and congenital heart defect. These findings highlight the importance of improving the DDS for maternal and infant health.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".