Periconceptional use of vitamin A and the risk of giving birth to a child with <scp>nonsyndromic</scp> orofacial clefts—A <scp>meta‐analysis</scp>
Bibliographic record
Abstract
BACKGROUND: We conducted a meta-analysis of observational epidemiological studies to evaluate the association between periconceptional use of vitamin A and the risk of giving birth to a child with nonsyndromic orofacial clefts (NSOFCs). METHODS: We carried out a systematic literature search of Embase, PubMed, Web of Science, Google Scholar, and OpenGrey from inception to June 30, 2021. Two reviewers independently evaluated the studies that met the inclusion criteria and filled out an abstraction form for each study. Study quality was assessed using the Newcastle-Ottawa Assessment Scale (NOS). Adjusted estimates were pooled with an inverse variance weighting using a random-effects model. Heterogeneity and publication bias were assessed using the Cochran's Q test and funnel plot, respectively. RESULTS: A total of six case-control studies with moderate risk of bias were included. The pooled OR showed a 20% reduction in the risk of NSOFCs for periconceptional use of vitamin A which was not statistically significant (OR = .80; 95% CI .54-1.17, p = .25). For nonsyndromic cleft lip with or without cleft palate (NSCL/P), the studies were homogenous, and the pooled estimate showed a 13% risk reduction, which was significant (OR = .87; 95% CI .77-.99, p = .03). For nonsyndromic cleft palate only (NSCPO), the pooled estimate showed a 33% lower likelihood, which was not statistically significant (OR = .67; 95% CI .42-1.08, p = .10). CONCLUSION: Our results suggest a possible protective effect for the periconceptional use of vitamin A on the risk of NSCL/P. This finding should be investigated further in prospective studies across multiple populations.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.031 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".