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Additional file 2: Table S2. of A comprehensive evaluation of interaction between genetic variants and use of menopausal hormone therapy on mammographic density

2015· article· en· W3174142230 on OpenAlexaboutno aff
Anja Rudolph, Peter Fasching, Sabine Behrens, Ursula Eilber, Manjeet K. Bolla, Qin Wang, Deborah J. Thompson, Kamila Czene, Judith S. Brand, Jingmei Li, Christopher G. Scott, V. Shane Pankratz, Kathleen R. Brandt, Emily Hallberg, Janet E. Olson, Adam Lee, Matthias W. Beckmann, Arif B. Ekici, Lothar Haeberle, Gertraud Maskarinec, Loı̈c Le Marchand, Fredrick R. Schumacher, Roger L. Milne, Julia A. Knight, Carmel Apicella, Melissa C. Southey, Miroslav Kapuscinski, John L. Hopper, Irene L. Andrulis, Graham G. Giles, Christopher Haiman, Kay‐Tee Khaw, Robert Luben, Per Hall, Paul D.P. Pharoah, Fergus J. Couch, Douglas Easton, Isabel dos‐Santos‐Silva, Celine M. Vachon, Jenny Chang‐Claude

Bibliographic record

VenueFigshare · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreast cancerMammographyCohortEuropean Prospective Investigation into Cancer and NutritionGynecologyEthnic groupCohort studyBreast cancer screeningHormone replacement therapy (female-to-male)Cancer registryPopulationFamily medicineCancerProspective cohort studyOncologyDemographyObstetricsInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Selected characteristics of the study population by study. ABCFS Australian breast cancer family study, BBCC Bavarian breast cancer cases and controls, MCBS Mayo clinic breast cancer study, MCCS Melbourne collaborative cohort study, MEC Multi-ethnic cohort, MMHS Mayo mammography health study, OFBCR Ontario familial breast cancer registry, SASBAC Singapore and Sweden breast cancer study, EPIC European prospective investigation into cancer and nutrition, SIBS Sisters in breast screening study. (DOC 87 kb)

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.2740.000

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 head0.198
GPT teacher head0.337
Teacher spread0.139 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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