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Record W4281625486 · doi:10.3389/fendo.2022.897101

Advanced Paternal Age and Future Generations

2022· review· en· W4281625486 on OpenAlexafffund
Peter Chan, Bernard Robaire

Bibliographic record

VenueFrontiers in Endocrinology · 2022
Typereview
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsPaternal ageOffspringAdvanced maternal ageFertilityAffect (linguistics)Transgenerational epigeneticsMedicineBiologyDemographyPregnancyEnvironmental healthPsychologyPopulationGenetics

Abstract

fetched live from OpenAlex

Paternal age at conception has been increasing. In this review, we first present the results from the major mammalian animal models used to establish that increasing paternal age does affect progeny outcome. These models provide several major advantages including the possibility to assess multi- transgenerational effects of paternal age on progeny in a relatively short time window. We then present the clinical observations relating advanced paternal age to fertility and effects on offspring with respect to perinatal health, cancer risk, genetic diseases, and neurodevelopmental effects. An overview of the potential mechanism operating in altering germ cells in advanced age is presented. This is followed by an analysis of the current state of management of reproductive risks associated with advanced paternal age. The numerous challenges associated with developing effective, practical strategies to mitigate the impact of advanced paternal age are outlined along with an approach on how to move forward with this important clinical quandary.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.040
GPT teacher head0.332
Teacher spread0.292 · 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 designNot applicable
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

Citations37
Published2022
Admission routes2
Has abstractyes

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