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Record W2129363225 · doi:10.1309/ajcpatbz2jfn1qqc

<i>HER2</i>Gene Amplification in Breast Cancer

2012· article· en· W2129363225 on OpenAlex
Jane Starczynski, Neil Atkey, Yvonne Connelly, Tony O’Grady, Fiona Campbell, Silvana Di Palma, Peter Wencyk, Bharat Jasani, Michael Gandy, John M.S. Bartlett

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2012
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsInstitute of Cancer ResearchOntario Institute for Cancer Research
FundersUniversity of SurreyRoyal College of Surgeons in Ireland
KeywordsAuditBreast cancerConsistency (knowledge bases)CancerBiologyMedicinePathologyComputational biologyComputer scienceGeneticsAccountingArtificial intelligence

Abstract

fetched live from OpenAlex

International and national guidelines highlight the importance of accuracy, reproducibility, and quality control of in situ hybridization (ISH) methods for testing breast carcinomas. However, few guidelines cover the reporting of ISH cases with "unusual" signal patterns, including, eg, heterogeneity and loss of chromosome enumeration probe or gene signals. These cases are, in fact, relatively frequent, and there is a need for developing evidence- or consensus-based reporting guidelines to ensure consistency of treatment. Following an audit of cases from a single center (including >1,700 cases) we show that approximately 10% of ISH results reflect unusual signal patterns. We illustrate the most common of these patterns and provide reporting guidelines for diagnosticians and recommendations for future research. Our goal is to ensure that in the future such "rogues" are reported in a consistent manner that, ultimately, will be supported by molecular and biochemical evidence.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.167
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.077
GPT teacher head0.468
Teacher spread0.390 · 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