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Record W3112490627 · doi:10.1016/j.humpath.2020.12.003

The cutoff for estrogen and progesterone receptor expression in endometrial cancer revisited: a European Network for Individualized Treatment of Endometrial Cancer collaboration study

2020· article· en· W3112490627 on OpenAlexaff
Willem Jan van Weelden, Casper Reijnen, Heidi V.N. Küsters‐Vandevelde, Johan Bulten, Peter Bult, Samuel Leung, Nicole C.M. Visser, Marı́a Santacana, Peter Bronsert, Marc Hirschfeld, Eva Colás, Antonio Gil‐Moreno, Armando Reques, Gemma Mancebo, Jutta Huvila, Martin Koskas, Vít Weinberger, Markéta Bednaříková, Jitka Hausnerová, Marc P.L.M. Snijders, Xavier Matías‐Guiu, Frédéric Amant, Camilla Krakstad, Koen Van de Vijver, Jessica N. McAlpine, Johanna M.A. Pijnenborg

Bibliographic record

VenueHuman Pathology · 2020
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsUniversity of British ColumbiaVancouver General Hospital
Fundersnot available
KeywordsCutoffProgesterone receptorEstrogen receptorInternal medicineMedicineCohortOncologyEndometrial cancerEstrogenCancerGynecologyImmunohistochemistryCohort studySurvival analysisBreast cancer

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)0.080
metaresearch head score (Gemma)0.064
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 score0.080
Threshold uncertainty score0.422

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0800.064
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.078
GPT teacher head0.377
Teacher spread0.299 · 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

Citations40
Published2020
Admission routes1
Has abstractno

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