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The impact of Angelina Jolie's (AJ) story on genetic referral and testing at an academic cancer centre.

2014· article· en· W2278132373 on OpenAlexaffabout
Jacques Raphael, Sunil Verma, Paul Hewitt, Andrea Eisen

Bibliographic record

VenueJournal of Clinical Oncology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineFamily historyBreast cancerGenetic counselingReferralFamily medicineGenetic testingOvarian cancerChristian ministryCancerBRCA mutationGynecologyInternal medicine

Abstract

fetched live from OpenAlex

44 Background: In May 2013, AJ revealed to the media that she had undergone preventive double mastectomy. The actress had a family history of breast and ovarian cancer and tested positive for the BRCA1 gene mutation. Media coverage has been extensive, but it’s not clear what messages the public and professional medical staff took from this personal story that sometimes could be misleading. Methods: We conducted a retrospective review in our centre using data from the clinical database of the Familial Cancer Program in a tertiary care cancer centre. The impact of AJ’s story on genetic counseling referrals was assessed by comparing the number of referrals made 6 months before and after the story. In addition, the quality of referrals was reported by comparing the number of patients who qualified for genetic testing as defined by the Ontario Ministry of Health and Long Term Care and the ones who carried a BRCA1/2 mutation before and after the media release. Results: The number of women referred for genetic counseling increased by 85% after the release of AJ’s story (479 before versus 887 after). This translated to an increase of 99% in the number of women who qualified for a genetic testing (211 before versus 419 after). Among them, 120 and 254 women had a history of breast and ovarian cancer in their family, 16 and 37 women had a history of male breast cancer in their family, and 28 and 15 women were diagnosed with breast cancer at the age of 35 or less before and after AJ’s story respectively. Furthermore, the number of BRCA1/2 carriers identified increased by 107% (29 (14 BRCA1, 15 BRCA2) before and 60 (32 BRCA1, 28 BRCA2) after). Conclusions: This study clearly shows that the number of genetic referrals doubled after AJ’s story. Nevertheless, the quality of referral remained the same with nearly the same percentage of patients who qualified for genetic testing and who were identified as BRCA1/2 carriers. The challenge is to meet the increased demand for cancer genetic services including screening, counseling, testing, and preventive surgery. After AJ’s story the current model of genetic counseling may need to be revisited.

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)0.004
metaresearch head score (Gemma)0.048
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 score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.130
GPT teacher head0.489
Teacher spread0.360 · 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

Citations8
Published2014
Admission routes2
Has abstractyes

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