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Record W4313454758 · doi:10.1007/s10654-022-00921-1

Circulating vitamin D and breast cancer risk: an international pooling project of 17 cohorts

2023· article· en· W4313454758 on OpenAlexfundno aff
Kala Visvanathan, Alison M. Mondul, Anne Zeleniuch‐Jacquotte, Molin Wang, Mitchell H. Gail, Shiaw‐Shyuan Yaun, Stephanie J. Weinstein, Marjorie L. McCullough, A. Heather Eliassen, Nancy R. Cook, Claudia Agnoli, Martin Almquist, Amanda Black, Julie E. Buring, Chu Chen, Yu Chen, Tess V. Clendenen, Laure Dossus, Veronika Fedirko, Gretchen L. Gierach, Edward L. Giovannucci, Gary E. Goodman, Marc T. Goodman, Pascal Guénel, Göran Hallmans, Susan E. Hankinson, Ronald L. Horst, Tao Hou, Wen‐Yi Huang, Michael E. Jones, Corrine E. Joshu, Rudolf Kaaks, Vittorio Krogh, Tilman Kühn, Marina Kvaskoff, I‐Min Lee, Yahya Mahamat‐Saleh, Johan Malm, Jonas Manjer, Gertraud Maskarinec, Amy E. Millen, Toqir K Mukhtar, Marian L. Neuhouser, Trude Eid Robsahm, Minouk J. Schoemaker, Sabina Sieri, Malin Sund, Anthony J. Swerdlow, Cynthia A. Thomson, Giske Ursin, Jean Wactawski‐Wende, Ying Wang, Lynne R. Wilkens, Yujie Wu, Emilie S. Zoltick, Walter C. Willett, Stephanie A. Smith‐Warner, Regina G. Ziegler

Bibliographic record

VenueEuropean Journal of Epidemiology · 2023
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsnot available
FundersNational Institute of Environmental Health SciencesNational Cancer InstituteNational Heart, Lung, and Blood InstituteSchool of Public Health, Imperial College LondonNIHR Imperial Biomedical Research CentreInstituto de Salud Carlos IIIMedical Research CouncilCenters for Disease Control and PreventionVetenskapsrådetMutuelle Générale de l'Education NationaleMinisterio de Economía y CompetitividadCancerfondenInstitut National de la Santé et de la Recherche MédicaleAgence Nationale de la RechercheAssociazione Italiana per la Ricerca sul CancroImperial College LondonYork UniversityNational Institute on AgingNational Institute for Health and Care ResearchDivision of Cancer Prevention, National Cancer InstituteHarvard T.H. Chan School of Public HealthJohns Hopkins UniversityCancer Research UKWorld Health OrganizationBreast Cancer Research FoundationInstitut Gustave-RoussyBrigham and Women's HospitalLigue Contre le CancerAmerican Cancer SocietyNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsMedicineBreast cancerDecileVitamin D and neurologyInternal medicineCancerIncidence (geometry)Prospective cohort studyRelative riskCohort studyOncologyConfidence interval

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)0.008
metaresearch head score (Gemma)0.007
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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.005
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.092
GPT teacher head0.409
Teacher spread0.317 · 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

Citations32
Published2023
Admission routes1
Has abstractno

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