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Record W4295906221 · doi:10.1002/jgc4.1621

Practice resource‐focused revision: Standardized pedigree nomenclature update centered on sex and gender inclusivity: A practice resource of the National Society of Genetic Counselors

2022· review· en· W4295906221 on OpenAlexaff
Robin L. Bennett, Kathryn Steinhaus French, Robert G. Resta, Jehannine Austin

Bibliographic record

VenueJournal of Genetic Counseling · 2022
Typereview
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNomenclatureRelevance (law)TransgenderResource (disambiguation)Genetic counselingDiversity (politics)PsychologyMedicineMedical educationSociologyGeneticsComputer scienceBiologyPolitical scienceTaxonomy (biology)

Abstract

fetched live from OpenAlex

This focused revision builds on the expert opinions from the original publications of 'Recommendations for human standardized pedigree nomenclature' published in 1995 and updated in 2008. Our review of medical publications since 2008 did not identify any fundamental systematic alternative pedigree nomenclature. These findings attest to the relevance of most of the nomenclature with the critical exception of the nomenclature used to denote sex assigned at birth and gender. While we are not recommending the creation of any new pedigree symbols, a major focus of this publication is clarification of the use of symbols and language in the description of the distinction between sex and gender, with a view to ensuring safe and inclusive practice for people who are gender-diverse or transgender. In addition, we recommend modifications to the way that carrier status is depicted. Our goal is to respect individual differences and identities while maintaining biologically, clinically, and genetically meaningful information.

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)0.067
metaresearch head score (Gemma)0.187
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: Methods · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.355

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.187
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.006
Science and technology studies0.0020.003
Scholarly communication0.0040.006
Open science0.0050.005
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0090.007

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 head0.083
GPT teacher head0.411
Teacher spread0.329 · 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
GenreMethods

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

Citations59
Published2022
Admission routes1
Has abstractyes

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