MétaCan
Menu
Back to cohort
Record W2154671238 · doi:10.1016/j.breast.2012.12.004

Experts' opinion: Recommendations for retesting breast cancer metastases for HER2 and hormone receptor status

2013· article· en· W2154671238 on OpenAlexaff
Frédérique Penault‐Llorca, Renata A. Coudry, Wedad Hanna, R. Yoshiyuki Osamura, Josef Rüschoff, Giuseppe Viale

Bibliographic record

VenueThe Breast · 2013
Typearticle
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
FundersF. Hoffmann-La Roche
KeywordsMedicineHormone receptorOncologyBreast cancerHuman Epidermal Growth Factor Receptor 2HormoneMetastatic breast cancerInternal medicineCancerRegimenHormone therapyReceptor

Abstract

fetched live from OpenAlex

The human epidermal growth factor receptor 2 (HER2) and hormone receptor status of recurrent breast cancer may change between the tumor and metastases from negative to positive and vice versa, potentially affecting the treatment regimen. Retesting of metastases may therefore be crucial to allow appropriate selection of patients for whom targeted therapy is indicated; however, retesting is not routinely performed. This article recommends that metastases be retested for HER2 and hormone receptor status and provides practical guidance on when and how to retest, as agreed by a panel of expert pathologists with extensive experience of HER2 and hormone receptor testing.

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)0.021
metaresearch head score (Gemma)0.114
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.032
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.114
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0030.004
Open science0.0060.002
Research integrity0.0320.019
Insufficient payload (model declined to judge)0.0090.017

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 head0.087
GPT teacher head0.396
Teacher spread0.309 · 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
GenreCommentary

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

Citations21
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThe BreastSame topicHER2/EGFR in Cancer ResearchFrench-language works237,207