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Evaluation of the needs of spouses of female carriers of mutations in <i>BRCA1</i> and <i>BRCA2</i>

2002· article· en· W1512101045 on OpenAlexaffabout
Kelly Metcalfe, Alexander Liede, Martina Trinkaus, Danielle Hanna, SA Narod

Bibliographic record

VenueClinical Genetics · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsCoalition for Research in Women's HealthUniversity of Toronto
Fundersnot available
KeywordsSpouseGenetic counselingDistressWifeMedicineGenetic testingQuarter (Canadian coin)PsychologyClinical psychologyDemographyGeneticsInternal medicineBiology

Abstract

fetched live from OpenAlex

The process of genetic testing involves the entire family, including spouses. The objective of this study was to measure the specific needs and to describe the experiences of spouses of women who received genetic counseling for a positive BRCA1/2 result. We surveyed 59 spouses of female mutation carriers. The mean length of relationships was 26 years (range: 2.5-50 years). All were supportive of their spouses' decision to undergo genetic testing and counselling. Four respondents stated that they wished that they had received additional support at the time of test disclosure and 20% felt that their wives had received inadequate support. One-quarter of the spouses believed that their relationship had changed because of genetic testing; most felt that they had become closer to their wives. Husbands were most concerned about the risk of their wife dying of cancer (43%), followed by the risk of their spouse developing cancer (19%) and the risk that their children would test positive for the BRCA mutation (14%). Distress levels, measured by the Impact of Event scale, suggest that few spouses were experiencing clinical levels of distress.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.362
Teacher spread0.286 · 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 designObservational
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

Citations33
Published2002
Admission routes2
Has abstractyes

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