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Record W3127622668 · doi:10.1111/tbj.14185

Abnormal screens among nonmutation carriers in the High Risk Ontario Breast Screening Program

2021· article· en· W3127622668 on OpenAlexafffundabout
Matthew Castelo, Zachary M. Brown, Angela E. Schellenberg, Jane K. Mills, Andrea Eisen, Derek Muradali, Eva Grunfeld, Adena Scheer

Bibliographic record

VenueThe Breast Journal · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreSelkirk CollegePublic Health OntarioUniversity of TorontoToronto General HospitalSt. Michael's Hospital
FundersCancer Care Ontario
KeywordsMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: The Ontario Breast Screening Program was expanded in 2011 to offer annual MRI and mammography to women with high-risk genetic mutations (e.g., BRCA1/2) and women with strong family histories and ≥25% estimated lifetime risk of breast cancer. Data to support high-risk screening is less clear in the nonmutation carrier group, as MRI has lower specificity among this population. The potential unintended consequences may be considerable and need to be explored. We aimed to describe the frequency of abnormal screens and biopsies. METHODS: Demographic surveys and chart review consent were sent to a sample of 441 individuals enrolled in a high-risk screening program at two tertiary care hospitals in Toronto, Ontario. Retrospective cross-sectional chart review was undertaken for clinicopathologic data. The frequencies of abnormal screens and biopsies were calculated. RESULTS: One hundred sixty-nine nonmutation carriers were included. The majority were white, employed, and highly educated. The median International Breast Cancer Intervention Study lifetime risk of breast cancer was 28.0% (range 24.5%-89.0%). 108 individuals (64%) experienced at least 1 abnormal screen and 13 (8%) had 3 or more over a median 3 years of screening (range 1-6 years). Of 55 biopsies, 3 (5.5%) were malignant. The cancer detection rate was 8.4/1000 screens (95% CI 3.2-22.4). CONCLUSIONS: An MRI-based screening program for nonmutation carriers was effective at diagnosing breast cancer. However, this population experienced a high rate of abnormal screens and intervention. Further research is needed to improve the performance of MRI-based screening in these women.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.244
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.008
GPT teacher head0.245
Teacher spread0.237 · 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

Citations3
Published2021
Admission routes3
Has abstractyes

Explore more

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