Maximizing the response to Herceptin® therapy through optimal use and patient selection
Bibliographic record
Abstract
The aggressiveness of human epidermal growth factor receptor-2 (HER2)-positive breast cancer and the poor prognosis of women with this disease demand the availability of accurate and reliable tests for HER2 status and the optimization of HER2-targeted therapy. The distinctive clinical pattern of HER2-positive breast cancer underlines the importance of testing for HER2 status and efforts are ongoing to validate the two major methods in use-immunohistochemistry (IHC), which measures cell membrane HER2 expression, and fluorescence in situ hybridization (FISH), which measures gene copy number. Clinical trial results demonstrate that there is an association between strong HER2 overexpression (IHC 3+) and optimal response to therapy with the novel recombinant HER2 antibody Herceptin. High levels of concordance between IHC 3+ and FISH-positive status have been observed, and response to treatment with Herceptin is similar for patients whose breast cancers are IHC 3+ and those who are FISH-positive. Observations to date have led to the formulation of an algorithm for HER2 status determination and Herceptin use which recommends that: (i) the HER2 status of all women with breast cancer be determined at presentation, (ii) all IHC 3+ and FISH-positive patients with metastatic disease should receive Herceptin, (iii) Herceptin should be used early in the course of metastatic breast cancer and preferably first line, and (iv) Herceptin therapy should be continued until disease progression.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".