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Record W4416587794 · doi:10.1158/0008-5472.30699122

Supplementary Information from Common Germline Risk Variants Impact Somatic Alterations and Clinical Features across Cancers

2025· article· W4416587794 on OpenAlexfundno aff
Shinichi Namba, Yuki Saito, Yasunori Kogure, Tatsuo Masuda, Melissa L. Bondy, Puya Gharahkhani, Ines Gockel, Dominik Heider, Axel M. Hillmer, Janusz Jankowski, Stuart MacGregor, Carlo Maj, Beatrice Melin, Quinn T. Ostrom, Claire Palles, Johannes Schumacher, Ian Tomlinson, David C. Whiteman, Yukinori Okada, Keisuke Kataoka

Bibliographic record

Venuenot available
Typearticle
Language
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsnot available
FundersDivision of Cancer Epidemiology and Genetics, National Cancer InstituteGeriatric Research Education and Clinical CenterNIH Clinical CenterNational Cancer InstituteSchool of Medicine, New York UniversityCenters for Disease Control and PreventionUniversity of WashingtonYork UniversityPurdue UniversityBrigham and Women's Hospital
KeywordsGermlineSomatic cellGermline mutationDiseaseMutationCancer

Abstract

fetched live from OpenAlex

Figures S1–S11, Tables S1–S6, Supplementary Materials and Methods, Supplementary Notes, and Supplementary References

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 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)0.001
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.740
Threshold uncertainty score0.371

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.7400.077

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 head0.024
GPT teacher head0.430
Teacher spread0.407 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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