MétaCan
Menu
Back to cohort
Record W2171244965 · doi:10.1016/j.breast.2012.03.003

1st International consensus guidelines for advanced breast cancer (ABC 1)

2012· article· en· W2171244965 on OpenAlexaff
Fátima Cardoso, A. Costa, Larry Norton, David Cameron, Tanja Čufer, Lesley Fallowfield, Prudence A. Francis, Joseph Gligorov, S. Kyriakides, Nancy U. Lin, Olivia Pagani, Elżbieta Senkus, Christoph Thomssen, Matti Aapro, Jonas Bergh, Angelo Di Leo, M. Ziad Saghir, Patricia A. Ganz, Karen A. Gelmon, Aron Goldhirsch, Nadia Harbeck, Nehmat Houssami, Clifford A. Hudis, Bella Kaufman, Maria Leadbeater, Musa Mayer, A. Rodger, Hope S. Rugo, Virgilio Sacchini, George W. Sledge, Laura van’t Veer, Giuseppe Viale, Ian E. Krop, Eric P. Winer

Bibliographic record

VenueThe Breast · 2012
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineConsensus conferenceBreast cancerMedical physicsFamily medicineCancerInternal medicine

Abstract

fetched live from OpenAlex

The 1st international Consensus Conference for Advanced Breast Cancer (ABC 1) took place on November 2011, in Lisbon. Consensus guidelines for the management of this disease were developed. This manuscript summarizes these international consensus guidelines.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.019
metaresearch head score (Gemma)0.037
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.037
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0090.006
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0060.004
Research integrity0.0090.009
Insufficient payload (model declined to judge)0.0090.006

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 head0.083
GPT teacher head0.430
Teacher spread0.348 · 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
GenreOther

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

Citations299
Published2012
Admission routes1
Has abstractno

Explore more

Same venueThe BreastSame topicCancer Treatment and PharmacologyFrench-language works237,207