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Record W2125219284 · doi:10.1136/jclinpath-2013-201819

HER2 testing for breast carcinoma: recommendations for rapid diagnostic pathways in clinical practice

2013· article· en· W2125219284 on OpenAlexaff
Abeer M. Shaaban, Colin A. Purdie, John M.S. Bartlett, Robert C. Stein, Sally Lane, A Francis, Alastair M. Thompson, Sarah E. Pinder

Bibliographic record

VenueJournal of Clinical Pathology · 2013
Typearticle
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsMedicineBreast cancerTurnaround timeClinical trialHuman Epidermal Growth Factor Receptor 2OncologyAdjuvantDiseaseInternal medicineAdjuvant therapyIntensive care medicineMedical physicsCancerComputer science

Abstract

fetched live from OpenAlex

Human epidermal growth factor receptor 2 (HER2) testing is required for newly diagnosed breast cancer and advised for recurrent and metastatic breast cancer, to determine treatment planning using HER2-directed therapy in the neoadjuvant, adjuvant and advanced disease settings. Wide variation, nationally, in the turnaround time for HER2 testing may hinder equity of access for patients to both clinical trials and the timely implementation of HER2-directed therapy particularly in the neo-adjuvant setting. Process mapping from three recognised laboratories in the UK was applied to the logistics of HER2 testing in different geographic hub and spoke models. Consequently, recommendations for HER2 testing likely to facilitate access to clinical trials and timely patient care are presented.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.257
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.257
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.340
GPT teacher head0.541
Teacher spread0.201 · 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 teacher head, not a consensus.

Study designObservational
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

Citations17
Published2013
Admission routes1
Has abstractyes

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