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Record W4318022906 · doi:10.1016/j.modpat.2022.100085

Grade and Estrogen Receptor Expression Identify a Subset of No Specific Molecular Profile Endometrial Carcinomas at a Very Low Risk of Disease-Specific Death

2023· article· en· W4318022906 on OpenAlex
Amy Jamieson, Jutta Huvila, Derek S. Chiu, Emily F. Thompson, Stephanie Scott, Shannon Salvador, Danielle Vicus, Limor Helpman, Walter H. Gotlieb, Sarah Kean, Vanessa Samouëlian, Martin Köbel, Mary Kinloch, Carlos Parra-Harran, Saul Offman, Katherine Grondin, Julie Irving, Amy Lum, Janine Senz, Samuel Leung, Melissa K. McConechy, Marie Plante, Stefan Kommoss, David G. Huntsman, Aline Talhouk, C. Blake Gilks, Jessica N. McAlpine

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueModern Pathology · 2023
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsUniversité LavalUniversity of SaskatchewanUniversité de MontréalUniversity of ManitobaMcMaster UniversityUniversity of TorontoDalhousie UniversityUniversity of CalgaryMcGill UniversityUniversity of British Columbia
FundersCanadian Institutes of Health ResearchBC Cancer FoundationVancouver Coastal Health Research InstituteMichael Smith Health Research BC
KeywordsLymphovascular invasionOncologyInternal medicineEstrogen receptorHazard ratioDiseaseUnivariate analysisStage (stratigraphy)MedicineProgesterone receptorImmunohistochemistryPathologyMultivariate analysisBiologyCancerBreast cancerMetastasisConfidence 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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.284
Teacher spread0.245 · 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