The influence of paternal preconception health on neonatal live birth and stillbirth outcomes: A scoping review
Bibliographic record
Abstract
INTRODUCTION: While evidence exists related to the impact of maternal preconception health on live birth and stillbirth outcomes, there is a lack of evidence on the role of paternal preconception health. OBJECTIVE: To identify, consolidate, and analyze the literature on paternal preconception health related to live births and stillbirths. DATA SOURCES: Medline, PsycINFO, Embase, Scopus and CINAHL databases were searched from January 1, 2013 through June 30, 2024. STUDY ELIGIBILITY CRITERIA: A scoping review was conducted following JBI methodology. Two independent reviewers screened titles, abstracts, and full texts. Data extraction was performed using a standardized form in Covidence. STUDY APPRAISAL & SYNTHESIS METHODS: Paternal health was synthesized into physical well-being, substance use, environmental exposures, mental health, and treatment effects. RESULTS: Of the 7690 articles identified in the search, 39 studies were deemed eligible, with 31 articles reporting on live birth and ten on stillbirth. Most studies were conducted in the United States (n = 19, 48.7 %), followed by China (n = 7, 17.9 %). There was a general dearth of studies to conclude any impact of paternal preconception health on live birth and stillbirth rates. Rather, findings were either mixed or limited to one or two studies per category. There were also several outcomes with no studies identified, indicating gaps in the literature as it relates to these paternal preconception health and live birth and stillbirth rates. CONCLUSIONS: Based on the limited number of studies and mixed findings, the impact of paternal preconception health on live births and stillbirth rates is unknown. These preliminary findings indicate an area ripe for future research.
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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 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 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".