Bibliographic record
Abstract
Incorporation of a new biological marker such as HER2 into routine clinical practice requires proof that it provides reproducible information independent of, and better than, conventional pathologic criteria, and that it influences treatment decisions. In breast cancer, HER2 amplification/overexpression predicts for a poor clinical outcome and an enhanced survival benefit from the HER2-targeted therapy, Herceptin, and may predict for resistance to some conventional therapies. Thus, HER2 is considered to be a clinically important molecule and testing for HER2 abnormalities is already part of routine patient assessment in many parts of the world. There is currently no gold standard for HER2 testing. The main challenge is to standardize and technically validate HER2 testing methodologies. Immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) are the most common HER2 tests used, and show a high level of concordance. HER2 testing approaches based on the polymerase chain reaction (PCR) are under extensive investigation and appear promising. A Canadian HER2 testing algorithm designed to increase the validity and reproducibility of HER2 testing has been compiled. HER2-positive cases are defined as those with >10% of tumor cells with moderate/strong, complete membrane staining in the invasive component, by IHC. Confirmatory HER2 testing using either FISH or quantitative PCR is recommended for indeterminate cases. Additional studies are required to calibrate HER2 testing results to clinical outcome.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".