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Record W2207530733 · doi:10.1016/j.canep.2015.11.015

International Consortium on Mammographic Density: Methodology and population diversity captured across 22 countries

2015· article· en· W2207530733 on OpenAlexaff
Valerie McCormack, Anya Burton, Isabel dos‐Santos‐Silva, John H. Hipwell, Caroline Dickens, Dorria Salem, Rasha Kamal, Mikael Hartman, Charmaine Pei Ling Lee, Kee-Seng Chia, Vahit Özmen, Anath Flugelman, Martín Lajous, Ruy Lopez-Riduara, Megan S. Rice, Isabelle Romieu, Giske Ursin, Samera Azeem Qureshi, Huiyan Ma, Eunjung Lee, Carla H. van Gils, Johanna O. P. Wanders, Sudhir Vinayak, Rose Ndumia, Steve Allen, Sarah Vinnicombe, Sue Moss, Jong Won Lee, Jisun Kim, Ana Pereira, María Luisa Garmendia, Reza Sirous, Mehri Sirous, Beata Pepłońska, Agnieszka Bukowska, Rulla M. Tamimi, Kimberly A. Bertrand, Chisato Nagata, Ava Kwong, Celine M. Vachon, Christopher G. Scott, Beatriz Pérez‐Gómez, Marina Pollán, Gertraud Maskarinec, Graham G. Giles, John L. Hopper, Jennifer Stone, Nadia Rajaram, Soo‐Hwang Teo, Shivaani Mariapun, Martin J. Yaffe, Joachim Schüz, Anna M. Chiarelli, Linda Linton, Norman F. Boyd

Bibliographic record

VenueCancer Epidemiology · 2015
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsCancer Care OntarioPrincess Margaret Cancer CentreUniversity of Toronto
FundersNational Cancer InstituteNational Medical Research CouncilCancer Council VictoriaNational Institutes of HealthMedical Research CouncilInstituto de Seguriidad y Servicios Sociales de los Trabadores del EstadoUniversiti MalayaZonMwWorld Health OrganizationEuropean CommissionNational Institute for Health and Care ResearchIsfahan University of Medical SciencesBreast Cancer CampaignNational Health and Medical Research CouncilCancer Research UKSusan G. Komen for the CureEngineering and Physical Sciences Research CouncilIsrael Cancer AssociationWorld Cancer Research FundNational Breast Cancer FoundationEllison Medical Foundation
KeywordsMedicineConfoundingBreast cancerEpidemiologyPopulationDemographyMammographyTraitEnvironmental healthCancerInternal medicine

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)0.005
metaresearch head score (Gemma)0.010
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 score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.009
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.176
GPT teacher head0.414
Teacher spread0.239 · 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

Citations28
Published2015
Admission routes1
Has abstractno

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