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Record W2112258659 · doi:10.1111/cge.12468

Strategies for recruitment of relatives of <scp>BRCA</scp> mutation carriers to a genetic testing program in the Bahamas

2014· article· en· W2112258659 on OpenAlexaff
Magan Trottier, John Lunn, Raleigh Butler, D. Curling, Theodore Turnquest, Robert E. Royer, Mohammad R. Akbari, Talia Donenberg, Judith Hurley, Steven A. Narod

Bibliographic record

VenueClinical Genetics · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsPublic Health OntarioWomen's College HospitalUniversity of Toronto
FundersSusan G. Komen for the Cure
KeywordsProbandGenetic counselingGenetic testingMedicineMutationBRCA mutationGeneticsFamily historyPsychological interventionCancerBreast cancerFamily medicinePsychiatryBiologyInternal medicineGene

Abstract

fetched live from OpenAlex

The prevalence of BRCA1 and BRCA2 mutations among unselected breast cancer patients in the Bahamas is 23%. It is beneficial to advise relatives of mutation carriers that they are candidates for genetic testing. Women who test positive are then eligible for preventive interventions, such as oophorectomy. It is not clear how often relatives of women with a mutation in the Bahamas wish to undergo genetic testing for the family mutation. Furthermore, it is not clear how best to communicate this sensitive information to relatives in order to maximize patient compliance. We offered genetic testing to 202 first-degree relatives of 58 mutation carriers. Of 159 women who were contacted by the proband or other family member, only 14 made an appointment for genetic testing (9%). In contrast, among 32 relatives who were contacted directly by the genetic counselor, 27 came for an appointment (84%). This study suggests that for recruitment of relatives in the Bahamas, direct contact by counselor is preferable to using the proband as an intermediary.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.121
GPT teacher head0.418
Teacher spread0.297 · 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 teacher head, 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

Citations29
Published2014
Admission routes1
Has abstractyes

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