Prediction of Breast Cancer Risk Based on Profiling With Common Genetic Variants
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Simulation or modelingConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.856
- Threshold uncertainty score
- 0.463
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.265 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
BACKGROUND: Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking. METHODS: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates. RESULTS: There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer. CONCLUSIONS: The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.
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.
The record
- Venue
- JNCI Journal of the National Cancer Institute
- Topic
- BRCA gene mutations in cancer
- Field
- Biochemistry, Genetics and Molecular Biology
- Canadian institutions
- Université LavalUniversité de MontréalMcGill UniversityUniversity Health NetworkUniversity of Toronto
- Funders
- Division of Cancer Epidemiology and Genetics, National Cancer InstituteMedical Research and Materiel CommandMedical Research CouncilMinistero dello Sviluppo EconomicoAgence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du TravailRadboud Universitair Medisch CentrumNational Health and Medical Research CouncilOulun YliopistoDeutsche KrebshilfeMedizinischen Hochschule HannoverNorges ForskningsrådUniversitair Medisch Centrum GroningenCenters for Disease Control and PreventionInstitut National Du CancerLeids Universitair Medisch CentrumAssociazione Italiana per la Ricerca sul CancroKWF KankerbestrijdingVetenskapsrådetStockholms Läns LandstingLigue Contre le CancerKuopion Yliopistollinen SairaalaKarolinska InstitutetHerlev HospitalCanadian Institutes of Health ResearchGeneral Secretariat for Research and TechnologySundhed og Sygdom, Det Frie ForskningsrådMinistry of Education and Science of the Russian FederationBundesministerium für Bildung und ForschungMinisterio de Economía y CompetitividadDeutsche Gesetzliche UnfallversicherungNederlandse Organisatie voor Wetenschappelijk OnderzoekRussian Foundation for Basic ResearchMinistère du Développement Économique, de l’Innovation et de l’ExportationLon V. Smith FoundationRadboud UniversiteitUniversiteit LeidenRobert Bosch StiftungFonds Wetenschappelijk OnderzoekCancerfondenNational Cancer InstituteCancer Institute NSWNational Breast Cancer FoundationEuropean CommissionAcademy of FinlandKing's College LondonRoswell Park Cancer InstituteNational Institute for Health and Care ResearchCancer Research UKVrije Universiteit AmsterdamErasmus Universiteit RotterdamMemorial Sloan-Kettering Cancer CenterFondation du cancer du sein du QuébecGenome CanadaItä-Suomen YliopistoAgency for Science, Technology and ResearchDavid F. and Margaret T. Grohne Family FoundationDeutsches KrebsforschungszentrumCancer Council VictoriaCalifornia Department of Public HealthFondation de FranceNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchUniversity of CambridgeGovernment of CanadaHelsingin ja Uudenmaan SairaanhoitopiiriVanderbilt UniversityGénome QuébecAgence Nationale de la RechercheU.S. Department of Health and Human ServicesMayo ClinicBreast Cancer Research FoundationSusan G. Komen for the CureU.S. ArmyUniversity of WestminsterCancer Council TasmaniaFrancis Crick InstituteErasmus Medisch CentrumNational Institutes of HealthVanderbilt-Ingram Cancer Center
- Keywords
- Breast cancerProfiling (computer programming)OncologyComputational biologyInternal medicineMedicineBiologyCancerComputer science
- Has abstract in OpenAlex
- yes