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Record W2120431466 · doi:10.1093/annonc/mdu450

The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014

2014· article· en· W2120431466 on OpenAlexaff
Roberto Salgado, Carsten Denkert, Sandra Demaria, Nicolas Sirtaine, Frederick Klauschen, Giancarlo Pruneri, Stephan Wienert, Gert Van den Eynden, Frederick L. Baehner, Frédérique Penault‐Llorca, Edith A. Perez, E. Aubrey Thompson, W. Fraser Symmans, Andrea L. Richardson, Jane Brock, Carmen Criscitiello, Helen D. Bailey, M. Ignatiadis, Giuseppe Floris, Joseph A. Sparano, Zuzana Kos, Torsten O. Nielsen, David L. Rimm, Kimberly H. Allison, Jorge S. Reis‐Filho, Sibylle Loibl, Christos Sotiriou, Giuseppe Viale, Sunil Badve, Sylvia Adams, Karen Willard‐Gallo, Sherene Loi

Bibliographic record

VenueAnnals of Oncology · 2014
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaUniversity Health Network
Fundersnot available
KeywordsMedicineTumor-infiltrating lymphocytesH&E stainBreast cancerOncologyMedical physicsCancerPathologyInternal medicineImmunotherapyStaining

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)0.049
metaresearch head score (Gemma)0.033
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.261

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0040.003
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0070.004
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0010.002

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 head0.090
GPT teacher head0.424
Teacher spread0.334 · 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
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

Citations3,136
Published2014
Admission routes1
Has abstractno

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