Breastfeeding and Cleft Lip and Palate: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between cleft lip and/or cleft palate (CL/P) and breastfeeding (BF). DESIGN: A systematic review and meta-analysis were performed based on studies published in PubMed, Scopus, Web of Science, Cochrane Library, LILACS, BBO, and Embase databases, and in the gray literature. The search occurred in September 2021 and was updated in March 2022. Observational studies evaluating the association between BF and CL/P were included. Risk of bias was analyzed using the Newcastle-Ottawa Scale. A random-effects meta-analysis was conducted. Certainty of evidence was evaluated using the GRADE approach. MAIN OUTCOME MEASURE(S): Frequency of BF in relation to the presence or absence of CL/P, as well as to the type of CL/P. The association between cleft type and BF challenges was also evaluated. RESULTS: From a total of 6863 studies identified, 29 were included in the qualitative review. Risk of bias was moderate and high in most studies (n = 26). There was a significant association between the presence of CL/P and absence of BF (OR = 18.08; 95% CI 7.09-46.09). Individuals with cleft palate with or without cleft lip (CP ± L) had a significantly lower frequency of BF (OR = 5.93; 95% CI 4.30-8.16) and a significantly higher frequency of BF challenges (OR = 13.55; 95% CI 4.91-37.43) compared to individuals with CL. Certainty of the evidence was low or very low in all analyses. CONCLUSION: The presence of clefts, especially those with palate involvement, is associated with higher chances of absence of BF.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.036 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".