Effect of paternal health on pregnancy loss—A review of current evidence
Bibliographic record
Abstract
Pregnancy loss has multifactorial causes, and the maternal risk factors are the most investigated. Therefore, this review investigates the current literature regarding the effect of paternal health on pregnancy loss. This review is conducted according to the PRISMA guidelines. The electronic databases PubMed and Medline were the primary sources of information. The online tool covidence.org was used for the screening process. The Newcastle-Ottawa Scale was used for assessment of risk of bias across the non-RCT (Randomized Controlled Trials) included studies. Six cohort studies and one randomised clinical trial were included for assessment in this review. Especially three large retrospective studies reported that circulatory paternal health issue, increasing metabolic syndrome diagnoses and paternal age was significantly associated with a higher risk of pregnancy loss. Lower pregnancy loss was also found in couples with diabetes in the man compared to couples without diabetes. One study suggests a connection between varicocelectomy and improved sperm DNA fragmentation and lower abortion rate. This review confirms that paternal age, somatic health and particularly health regarding cardiovascular and metabolic disease are associated positively with risks of pregnancy loss. However, further research may lead to evidence, which are more conclusive.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".