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Record W3046552718 · doi:10.1093/humrep/deaa105

Association between advanced paternal age and congenital heart defects: a systematic review and meta-analysis

2020· review· en· W3046552718 on OpenAlexaboutno aff
F Joinau-Zoulovits, Nathalie Bertille, Jérémie F. Cohen, Babak Khoshnood

Bibliographic record

VenueHuman Reproduction · 2020
Typereview
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOdds ratioMedicineObservational studyMeta-analysisConfidence intervalCongenital malformationsPopulationPregnancyPediatricsDemographyInternal medicineGeneticsBiology

Abstract

fetched live from OpenAlex

STUDY QUESTION: Is there an association between advanced paternal age and congenital heart defects (CHD)? SUMMARY ANSWER: Advanced paternal age is associated with a 16% increase in the overall odds of CHD. WHAT IS KNOWN ALREADY: CHD are the most common congenital malformations. Several risk factors for CHD have been identified in the literature, but the association between advanced paternal age and CHD remains unclear. STUDY DESIGN, SIZE, DURATION: We conducted a systematic literature search on MEDLINE and EMBASE (1960-2019) to identify studies assessing the association between advanced paternal age (≥35 years) and the risk of CHD, unrestrictive of language or sample size. We used a combination of Medical Subject Headings (MeSH) terms and free text words such as 'paternal age', 'paternal factors', 'father's age', 'parental age', 'heart', 'cardiac', 'cardiovascular', 'abnormalities, congenital', 'birth defects', 'congenital malformations' and 'congenital abnormalities'. PARTICIPANTS/MATERIALS, SETTING, METHODS: We included observational studies aiming at assessing the association between paternal age and CHD. The included population could be live births, fetal deaths and terminations of pregnancy for fetal anomaly. To be included, studies had to provide either odds ratios (OR) with their 95% confidence interval (CI) or sufficient information to recalculate ORs with 95% CIs per paternal age category. We excluded studies if they had no comparative group and if they were reviews or case reports. Two independent reviewers selected the studies, extracted the data and assessed risk of bias using a modified Newcastle-Ottawa Scale. We used random-effects meta-analysis to produce summary estimates of crude OR. Associations were also tested in subgroups. MAIN RESULTS AND THE ROLE OF CHANCE: Of 191 studies identified, we included nine studies in the meta-analysis (9 917 011 participants, including 34 447 CHD), including four population-based studies. Five studies were judged at low risk of bias. Only one population-based study specifically investigated isolated CHD. The risk of CHD was higher with advanced paternal age (summary OR 1.16, 95% CI, 1.07-1.25). Effect sizes were stable in population-based studies and in those with low risk of bias. LIMITATIONS AND REASONS FOR CAUTION: The available evidence did not allow to assess (i) the risk of isolated CHD in population-based studies, (ii) the association between paternal age and the risk for specific CHD and (iii) the association between paternal age and CHD after adjustment for other risk factors, such as maternal age. WIDER IMPLICATIONS OF THE FINDINGS: Our findings suggest that advanced paternal age may be a risk factor for CHD. However, because the association is modest in magnitude, its usefulness as a criterion for targeted screening for CHD seems limited. STUDY FUNDING/COMPETING INTEREST(S): None. PROSPERO REGISTRATION NUMBER: CRD42019135061.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.030
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0180.031
Bibliometrics0.0050.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.103
GPT teacher head0.373
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations22
Published2020
Admission routes1
Has abstractyes

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