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Record W3012448416

Analytical validation of a standardised scoring protocol for Ki67 immunohistochemistry on breast cancer excision whole sections: an international multicentre collaboration

2019· book-chapter· en· W3012448416 on OpenAlexaff
Samuel Leung, Torsten O. Nielsen, Lila Zabaglo, Indu Arun, Sunil Badve, Anita Bane, John M.S. Bartlett, Signe Borgquist, Martin C. Chang, Andrew Dodson, Anna Ehinger, Susan Fineberg, Cornelia M. Focke, Dongxia Gao, Allen M. Gown, Carolina Gutiérrez, Judith Hugh, Zuzana Kos, Anne‐Vibeke Lænkholm, Mauro G. Mastropasqua, Takuya Moriya, Sharon Nofech‐Mozes, C. Kent Osborne, Frédérique Penault‐Llorca, Tammy Piper, Takashi Sakatani, Roberto Salgado, Jane Starczynski, Tomoharu Sugie, Bert van der Vegt, Giuseppe Viale, Daniel F. Hayes, Lisa M. McShane, Mitch Dowsett

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science CentreOttawa HospitalUniversity of OttawaUniversity of AlbertaInstitute of Cancer ResearchOntario Institute for Cancer ResearchMcMaster UniversityUniversity of British Columbia
Fundersnot available
KeywordsConcordanceIntraclass correlationBreast cancerMedicineReproducibilityConfidence intervalNuclear proliferationConcordance correlation coefficientProtocol (science)Nuclear medicineCancerPathologyInternal medicineStatisticsMathematics
DOInot available

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.235
metaresearch head score (Gemma)0.145
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.235
Threshold uncertainty score0.943

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2350.145
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.004
Science and technology studies0.0030.004
Scholarly communication0.0030.001
Open science0.0050.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.388
Teacher spread0.367 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2019
Admission routes1
Has abstractyes

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