Bibliographic record
Abstract
HER2 status is of great clinical value in breast tumors for the identification of those patients who are eligible for trastuzumab therapy. There is a debate about the advantages and disadvantages of immunohistochemistry (IHC) and fluorescent in situ hybridization (FISH) and where, when, how, and who would perform HER2 testing. The purpose of this study was to analyze clinical and analytic validity and clinical utility for HER2 testing (IHC and FISH) for the appropriate selection of breast cancer patients who were suitable for trastuzumab therapy. A systematic review of the literature was carried out using online databases (MedLine, Embase, Centre for Reviews and Dissemination and the Cochrane Library). It brings up to date the cost-effectiveness analysis published by Technology Assessment Unit of the McGill University Health Centre (MUHC). The articles that were selected according to the following inclusion criteria: (1) included breast cancer patients, (2) used IHC and FISH assays, (3) reported the sensibility, specificity, reliability of IHC and FISH, or concordance between both techniques, and (4) were published in any language. A critical appraisal was performed using an ad hoc scale based on CASPe (Critical Appraisal Skills Programme Spanish) criteria. Literature search generated 17 studies that satisfied the inclusion criteria. Most articles evaluated the HercepTest and PathVysion assays. FISH was considered the gold standard test. We calculated the FISH positivity rate in each IHC score category. The percentage of FISH positive results (median) was estimated as 3.5 for IHC0, 5.8 for IHC1+, 17 for IHC2+, and 83.5 for IHC3+. Our findings have shown high concordance rates between IHC and FISH in tumors IHC0 and IHC1+, and discordance rates among cases with IHC2+ and IHC3+. In these cases, FISH is considered gold standard for confirming or excluding HER2 amplification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".