[Her-2/neu analysis--new data?].
Bibliographic record
Abstract
Her-2 status determination is an essential prerequisite before considering patient eligibility for treatment with trastuzumab. Currently the most common techniques to assess Her-2 status in routine practice are immunohistochemistry (IHC) and dual color FISH for receptor expression and gene amplification analysis, respectively. Despite both methods are well-established in breast cancer there are a variety of yet unsolved questions: 1. Do we really need IHC since interlab variation is still quite high (up to 30%)? 2. Are FISH and CISH equivalent techniques? 3. Are there any precautions to be taken if Her-2 is tested in non-breast cancer samples? 4. What is the value of Her-2 status in blood serum (ELISA)? 5. Do we get better response prediction if new Her2 antibodies, other techniques such as quantitative (q) RT-PCR or multiparameter assays according to downstream signalling pathways are applied? 6. Is Her-2 status still predictive when other therapeutic antibodies than trastuzumab (e. g. pertuzumab) or kinase inhibitors (e. g. lapatinib) are used? These questions will be discussed under the review of the recent literature and under own experiences obtained either by centralized Her-2 assessment in a variety of breast and non-breast cancer therapy studies and within international ring studies between reference labs from Australia (M. Bilous), Canada (W. Hanna), France (F. Penault-Llorcoa), Great Britain (M. Dowsett), Japan (R. Y. Osamura), and Netherlands (M. v. d. Vijver) in which we participated.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.091 | 0.067 |
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".