MétaCan
Menu
Back to cohort
Record W4387297084 · doi:10.1089/jwh.2023.0663

Beyond October, Beyond Pink: A Year-Round Revelation for Women's Breast Health

2023· article· en· W4387297084 on OpenAlexaff
Rakesh Kumar, M. Marcelo Mardones, Luís Costa, Sunil Saini, Cynthia Villarreal‐Garza, Bertha Alejandra Martínez-Cannon, Geetha Manjunath, Saket Sinha, Zhiyong Han, Anshika Arora, Ana Magalhães Ferreira, Lorna Larsen, Sabine Hairabedian, Therese Curry, Hirondina Borge, Gilberto Amorim, Chikako Shimizu, Vaishali Zamre, Masakazu Toi, Paul B. Fisher, Robert Clarke, Allan Lipton, Miguel Martín, Ellen Warner

Bibliographic record

VenueJournal of Women s Health · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineRevelationBreast cancerLibrary scienceFamily medicineInternal medicineCancerTheologyPhilosophy

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)0.011
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0100.012
Scholarly communication0.0140.018
Open science0.0020.009
Research integrity0.0220.034
Insufficient payload (model declined to judge)0.0330.006

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 head0.062
GPT teacher head0.384
Teacher spread0.322 · 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 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

Citations10
Published2023
Admission routes1
Has abstractno

Explore more

Same venueJournal of Women s HealthSame topicGlobal Cancer Incidence and ScreeningFrench-language works237,207