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Record W4293860731 · doi:10.1093/ajcn/nqac229

Coffee consumption and risk of endometrial cancer: a pooled analysis of individual participant data in the Epidemiology of Endometrial Cancer Consortium (E2C2)

2022· review· en· W4293860731 on OpenAlexafffund
Marta Crous‐Bou, Mengmeng Du, Marc J. Gunter, Veronica Wendy Setiawan, Leo J. Schouten, Xiao‐Ou Shu, Nicolas Wentzensen, Kimberly A. Bertrand, Linda S. Cook, Christine M. Friedenreich, Susan M. Gapstur, Marc T. Goodman, Torukiri I Ibiebele, Carlo La Vecchia, Fabio Levi, Linda M. Liao, Eva Negri, Susan E. McCann, Kelly A. O’Connell, Julie R. Palmer, Alpa V. Patel, Jeanette Ponte, Peggy Reynolds, Carlotta Sacerdote, Rashmi Sinha, Amanda B. Spurdle, Britton Trabert, Piet A. van den Brandt, Penelope M. Webb, Stacey Petruzella, Sara H. Olson, Immaculata De Vivo

Bibliographic record

VenueAmerican Journal of Clinical Nutrition · 2022
Typereview
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsAlberta Health ServicesUniversity of Calgary
FundersNational Cancer InstituteAssociazione Italiana per la Ricerca sul CancroCanadian Institutes of Health ResearchCancer Council TasmaniaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Institutes of HealthNational Health and Medical Research CouncilAlberta Heritage Foundation for Medical ResearchNational Institute on AgingCancer Council QueenslandCentre International de Recherche sur le CancerWorld Health OrganizationCalifornia Breast Cancer Research ProgramRoswell Park Cancer Institute
KeywordsEndometrial cancerMedicineConfoundingEpidemiologyLogistic regressionLower riskCancerCohort studyGynecologyInternal medicineEnvironmental healthDemographyOncologyConfidence 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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.016
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.556
GPT teacher head0.583
Teacher spread0.027 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations19
Published2022
Admission routes2
Has abstractno

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