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Record W1971209307 · doi:10.1016/j.ctrv.2013.09.001

HER2 testing: Current status and future directions

2013· review· en· W1971209307 on OpenAlexaff
Edith A. Perez, Javier Cortés, Ana M. González-Angulo, John M.S. Bartlett

Bibliographic record

VenueCancer Treatment Reviews · 2013
Typereview
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsOntario Institute for Cancer Research
FundersGenentech
KeywordsMedicineClinical OncologyHuman Epidermal Growth Factor Receptor 2Medical physicsBreast cancerOncologyIntensive care medicineGynecologyCancerInternal medicine

Abstract

fetched live from OpenAlex

Accurate determination of human epidermal growth factor receptor 2 (HER2) status is critical for optimizing breast cancer outcomes. In 2007, the American Society of Clinical Oncology (ASCO) and the College of American Pathologists (CAP) developed guidelines for HER2 testing to reduce inaccuracy. However, current ASCO/CAP criteria may restrict access to HER2-targeted therapy for some patient groups who would derive a clear clinical benefit. ASCO/CAP are currently reviewing their guidelines to further optimize HER2 testing and include emerging techniques. Guidelines are critical for optimizing care, as is ongoing research into techniques that accurately and reproducibly assess HER2 status.

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)0.002
metaresearch head score (Gemma)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.335
GPT teacher head0.518
Teacher spread0.183 · 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
GenreReview

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

Citations161
Published2013
Admission routes1
Has abstractno

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