Association Between Iron, Folate, and Zinc Deficiencies During Pregnancy and Low Birth Weight: Systematic Review of Cohort Studies
Bibliographic record
Abstract
CONTEXT: Iron, folate, and zinc deficiencies during the gestational period may be associated with negative perinatal outcomes, such as low birth weight (LBW), but these relationships are not yet fully established in the scientific literature and require further investigation. OBJECTIVE: To systematically review the scientific production to investigate the association between iron, folate, and zinc deficiencies during pregnancy and LBW. DATA SOURCES: The search was carried out using high-sensitivity descriptors in the English, Portuguese, and Spanish languages, combined with Boolean operators, adapted to each of the following indexed databases: MEDLINE via PubMed, Embase, LILACS via BVS, CENTRAL, and Web of Science. The eligibility criteria followed the PECOS (population, exposure, comparator, outcome, study) strategy. DATA EXTRACTION: Data extraction was performed using an Excel spreadsheet with the study variables of interest. Subsequently, the information was analyzed and summarized in a table. The Newcastle-Ottawa Scale was used to perform the risk-of-bias analysis. DATA ANALYSIS: A total of 21 042 references were identified, of which 7169 related to folate, 6969 to iron, and 6904 to zinc. After eligibility criteria application, 37 articles were included in this study, of which 18 referred to zinc nutritional status, 10 related to iron, and 9 related to folate. Studies of iron (40%), folate (66.66%), and zinc (50%) revealed a positive association between deficiencies of these micronutrients and LBW. The overall methodological quality of the studies included in this review was considered high. CONCLUSIONS: Iron, folate, and zinc deficiencies are still present during gestation. Nevertheless, the association between deficiencies of these micronutrients and LBW is still contradictory, and more studies are needed, as is efficient nutritional monitoring before and during gestation. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42021284683.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".