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Record W2473466080 · doi:10.1093/ajcp/140.suppl1.093

Centralized Breast Cancer Biomarker Testing: A Value-Added Role in Guiding Patient Management

2013· article· en· W2473466080 on OpenAlexaff
Ananta Gurung, Chen Zhou, Amir Rahemtulla, Gregory J. Naus, Diana Beşliu-Ionescu, Malcolm Hayes, Kathy Ceballos, Tadaaki Hiruki, Helga Klein-Parker, Thomas A. Thomson, Dirk van Niekerk

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsBiomarkerBreast cancerMedicineCancerValue (mathematics)OncologyIntensive care medicineInternal medicineComputer scienceBiology

Abstract

fetched live from OpenAlex

A characteristic of breast biomarker testing in British Columbia is its extensive centralization at the British Columbia Cancer Agency (BCCA). For each block submitted to BCCA for biomarker testing, a hematoxylin and eosin (H&E)-stained slide is reviewed, and if deemed appropriate, assays for estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 (HER2) are performed. Since biomarker testing centralization a number of discrepant results were encountered, this study was conducted to determine reasons for discrepancies. Prospectively (over 1-year period), biomarker testing was performed on 2,439 patients. Two hundred fifty-three cases involved solely testing for HER2 gene expression with fluorescence in situ hybridization, so the total number of cases examined was 2,186. Discrepant diagnoses and reasons for disagreements were recorded. The discrepancy rate from the 2186 cases was approximately 0.9% (19 cases): 11 cases were initially reported as invasive breast carcinoma, 7 cases as ductal carcinoma in situ (DCIS) and 1 case as benign. Twelve (63%) of 19 discrepant diagnoses arose from core biopsies. Eight invasive cases were changed to a noninvasive lesion (DCIS, atypical apocrine adenosis, sclerosing papillary lesion, sclerosing adenosis and complex sclerosing lesion) and three invasive cases were changed to another malignancy (pleomorphic lobular carcinoma, diffuse large B-cell lymphoma and plasma cell myeloma). Of seven discrepant cases involving DCIS, three showed evidence of invasion, whereas the remaining fourcases did not show evidence of DCIS (instead showed flat epithelial atypia, atypical ductal hyperplasia, atypical papillary lesion and benign breast parenchyma). One case which was initially reported as benign showed invasive carcinoma. Centralization of biomarker assays offers many benefits, including accredited standardized staining protocols and consistency with interpretation. It is an invaluable practice that may drastically affect patient care, ensuring patients are managed appropriately.

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)0.007
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.037
GPT teacher head0.347
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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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