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Record W3120827443 · doi:10.1007/s10549-020-06072-9

Breast cancer risk after age 60 among BRCA1 and BRCA2 mutation carriers

2021· article· en· W3120827443 on OpenAlexafffund
Neda Stjepanovic, Jan Lubiński, Pål Møller, Susan Randall Armel, William D. Foulkes, Nadine Tung, Susan L. Neuhausen, Joanne Kotsopoulos, Ping Sun, Sophie Sun, Andrea Eisen, Steven A. Narod, Leigha Senter, Charis Eng Fergus Couch, Robert Fruscio, Jeffrey N. Weitzel, Olufunmilayo Olopade, Christian F. Singer, Tuya Pal, Tomasz Huzarski, Cezary Cybulski, Kevin Sweet, Dana Zakalik, Marie Wood, Wendy McKinnon, Christine Elser, Georgia L. Wiesner, Eitan Friedman, Wendy S. Meschino, Carrie Snyder, Kelly Metcalfe, Aletta Poll, Ellen Warner, Raymond H. Kim, Rochelle Demsky, Peter Ainsworth, Linda Steele, Howard M. Saal, Kim Serfas, Seema Panchal, Carey A. Cullinane, Robert E. Reilly, Joanne L. Blum, Ava Kwong, Daniel Rayson, Teresa Ramón y Cajal, J.S. Dungan, Rinat Yerushalmi, Ophira Ginsburg, Intan Schraeder, Stephanie A. Cohen, Edmond LemireLemire, Stefania Zovato, Antonella Rastelli, Jacek Gronwald, Jeanna McCuaig, Beth Y. Karlan, Louise Bordeleau

Bibliographic record

VenueBreast Cancer Research and Treatment · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsBC Cancer AgencyPublic Health OntarioWomen's College HospitalUniversity Health NetworkUniversity of TorontoMcGill UniversitySunnybrook Health Science Centre
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health Research
KeywordsBreast cancerMedicineOncologyCancerMutationBRCA2 ProteinGynecologyInternal medicineBiologyGermline mutationGeneticsGene

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.018
GPT teacher head0.327
Teacher spread0.309 · 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

Citations12
Published2021
Admission routes2
Has abstractno

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