Advantages and Disadvantages of Technologies for HER2 Testing in Breast Cancer Specimens: Table 1
Bibliographic record
Abstract
OBJECTIVES: Human epidermal growth factor receptor 2 (HER2) plays a central role as a prognostic and predictive marker in breast cancer specimens. Reliable HER2 evaluation is central to determine the eligibility of patients with breast cancer to targeted anti-HER2 therapies such as trastuzumab and lapatinib. Presently, several methods exist for the determination of HER2 status at different levels (protein, RNA, and DNA level). METHODS: In this review, we discuss the main advantages and disadvantages of the techniques developed so far for the evaluation of HER2 status in breast cancer specimens. RESULTS: Each technique has its own advantages and disadvantages. It is therefore not surprising that no consensus has been reached so far on which technique is the best for the determination of HER2 status. CONCLUSIONS: Currently, emphasis must be put on standardization of procedures, internal and external quality control assessment, and competency evaluation of already existing methods to ensure accurate, reliable, and clinically meaningful test results. Development of new robust and accurate diagnostic assays should also be encouraged. In addition, large clinical trials are warranted to identify the technique that most reliably predicts a positive response to anti-HER2 drugs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".