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Record W2117775530 · doi:10.1093/annonc/mdt303

Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013

2013· article· en· W2117775530 on OpenAlexaff
Aron Goldhirsch, Eric P. Winer, Alan S. Coates, Richard D. Gelber, Martine Piccart, Beat Thürlimann, Hans-Jörg Senn, Kathy S. Albain, Fabrice André, Jonas Bergh, Hervé Bonnefoi, Denisse Bretel-Morales, Harold J. Burstein, Fátima Cardoso, Monica Castiglione‐Gertsch, Marco Colleoni, Alberto Costa, Giuseppe Curigliano, Nancy E. Davidson, Angelo Di Leo, Bent Ejlertsen, John Forbes, Michael Gnant, Pamela J. Goodwin, Paul E. Goss, Jay R. Harris, Daniel F. Hayes, Clifford A. Hudis, James N. Ingle, Jacek Jassem, Zefei Jiang, Per Karlsson, Sibylle Loibl, Monica Morrow, Moïse Namer, C. Kent Osborne, Ann H. Partridge, Frédérique Penault‐Llorca, Charles M. Perou, Kathleen I. Pritchard, Emiel J. Rutgers, Felix Sedlmayer, Semiglazov Vf, Zhi‐Ming Shao, Ian Smith, Masakazu Toi, Andrew Tutt, Michael Untch, Giuseppe Viale, Toru Watanabe, Nicholas Wilcken, William C. Wood

Bibliographic record

VenueAnnals of Oncology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsUniversity of Toronto
FundersNational Cancer Institute
KeywordsMedicineBreast cancerTrastuzumabOncologyCancerDiseaseRadiation therapyInternal medicineSurrogate endpointSystemic therapyBioinformatics

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)0.058
metaresearch head score (Gemma)0.055
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: Empirical · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.304

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0580.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0030.003
Science and technology studies0.0020.003
Scholarly communication0.0040.003
Open science0.0050.004
Research integrity0.0070.015
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 head0.033
GPT teacher head0.307
Teacher spread0.274 · 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

Citations3,715
Published2013
Admission routes1
Has abstractno

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