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Record W3136128311 · doi:10.1093/hropen/hoab009

A global approach to addressing the policy, research and social challenges of male reproductive health

2021· article· en· W3136128311 on OpenAlexaff
Christopher L. R. Barratt, Christopher J. De Jonge, Richard A. Anderson, Michael L. Eisenberg, Nicolás Garrido, Satu Rautakallio Hokkanen, Csilla Krausz, Sarah Kimmins, Moira K. O’Bryan, Allan Pacey, Frank Tüttelmann, Joris A. Veltman

Bibliographic record

VenueHuman Reproduction Open · 2021
Typearticle
Languageen
FieldMedicine
TopicSperm and Testicular Function
Canadian institutionsMcGill University
FundersNational Health and Medical Research CouncilNational Institutes of HealthMedical Research CouncilEuropean Society of Human Reproduction and EmbryologyDeutsche Forschungsgemeinschaft
KeywordsReproductive healthMale infertilityInfertilityOffspringSpecialtyGynecologyPolitical scienceMedicineFamily medicinePopulationEnvironmental healthPregnancyBiology

Abstract

fetched live from OpenAlex

Male infertility is a global health issue; yet to a large extent, our knowledge of its causes, impact and consequence is largely unknown. Recent data indicate that infertile men have an increased risk of somatic disorders such as cancer and die younger compared to fertile men. Moreover, several studies point to a significant adverse effect on the health of the offspring. From the startling lack of progress in male contraception combined with the paucity of improvements in the diagnosis of male infertility, we conclude there is a crisis in male reproductive health. The Male Reproductive Health Initiative has been organized to directly address these issues (www.eshre.eu/Specialty-groups/Special-Interest-Groups/Andrology/MRHI). The Working Group will formulate an evidence-based strategic road map outlining the ways forward. This is an open consortium desiring to engage with all stakeholders and governments.

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)0.031
metaresearch head score (Gemma)0.022
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: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.012
Scholarly communication0.0130.010
Open science0.0030.018
Research integrity0.0170.015
Insufficient payload (model declined to judge)0.0190.003

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 head0.419
GPT teacher head0.480
Teacher spread0.062 · 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
GenreEmpirical

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

Citations39
Published2021
Admission routes1
Has abstractyes

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