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Record W4293224604 · doi:10.1111/jocn.16498

Breast cancer genetic mutation: Synthesis of women's experience

2022· review· en· W4293224604 on OpenAlexaff
Nichola McNamara, Meghan Feeney, Martina Giltenane, Maura Dowling

Bibliographic record

VenueJournal of Clinical Nursing · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBreast cancerCINAHLBRCA mutationMedicineScopusMEDLINEQualitative researchFamily medicinePsychological interventionCancerGynecologyNursingInternal medicine

Abstract

fetched live from OpenAlex

AIMS AND OBJECTIVES: To systematically identify and synthesise the experiences reported by women with a breast cancer mutation who do not have cancer as reported in qualitative research published between 2013 and 2020. BACKGROUND: Women carrying a BReast CAncer (BRC) genetic mutation have an increased risk for breast and ovarian cancer. They must engage in emotional decision-making regarding risk management strategies to prevent cancer, including risk-reducing bilateral mastectomy and bilateral salpingo-oophorectomy. DESIGN AND METHODS: The ENTREQ statement guided this review. Eight databases were systematically searched (CINAHL, Pubmed, Embase, Psychinfo [Ovid], Web of Science, Scopus, Proquest and Lenus). Synthesis was guided by "best fit" framework. The Critical Appraisal Skills Programme guided assessment of methodological limitations and confidence in the review findings was informed by GRADE-CERQual. RESULTS: Twenty studies met the inclusion criteria for synthesis. Six themes were synthesised from the included studies (anxiety; family planning; it's a family affair; empowerment; actions; pragmatic adjustments). CONCLUSIONS: The multidimensional experiences of women living with a BRCA1/2 mutation require an individualised response based on women's needs at their life stages. A decision coaching model adopted during consultations could support women to guide decision-making regarding cancer risk-reducing strategies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.058
GPT teacher head0.447
Teacher spread0.389 · 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 designOther design
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

Citations7
Published2022
Admission routes1
Has abstractyes

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