A Scoping Review of the Relationship Between Maternal BMI and Offspring Incidence of Respiratory Infection: Where Do We Go From Here?
Bibliographic record
Abstract
Introduction: Pregnancy complications, including high maternal BMI, are associated with altered early development and child health outcomes. A growing body of work links the prenatal environment, specifically maternal BMI, with respiratory infections in offspring. In this rapid review, the authors review the literature supporting the hypothesis that high maternal BMI during pregnancy is associated with childhood respiratory infection incidence. Methods: The authors employed systematic search criteria in known databases-EMBASE, EMCARE, MEDLINE, CINAHL, and PsychINFO-searching from inception to January 2023. Included were primary research studies that involved (1) human pregnancy, (2) pregravid or gestational overweight or obesity, and (3) childhood respiratory infection with or without hospitalization. Results: Only 7 population-based cohort studies met the criteria, investigating maternal BMI as an exposure and childhood respiratory infection as an outcome (age 6 months to 18 years). Therefore, the authors conducted a qualitative analysis, and outcomes were reported. The authors found that >85% of the albeit few published studies support the hypothesis that maternal BMI may have independent and profound consequences on respiratory infection risk across childhood. Discussion: This area of research needs large-scale, well-controlled studies to better understand the relationship between maternal BMI and childhood respiratory infection. Possible resources such as cohort catalogs and combined databases are discussed. These findings add to the growing evidence that early environmental factors influence lifelong respiratory health. By incorporating a life course approach to infectious disease risk, policy makers can put this research to work and target health vulnerabilities before they arise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".