Consensus Recommendations on Estrogen Receptor Testing in Breast Cancer By Immunohistochemistry
Bibliographic record
Abstract
Estrogen receptor (ER) status in breast cancer is currently the most important predictive biomarker that determines breast cancer prognosis after treatment with endocrine therapy. Although immunohistochemistry has been widely viewed as the gold standard methodology for ER testing in breast cancer, lack of standardized procedures, and lack of regulatory adherence to testing guidelines has resulted in high rates of "false-negative" results worldwide. Standardized testing is only possible after all aspects of ER testing--preanalytical, analytical, and postanalytical, have been closely controlled. A meeting of the "ad-hoc committee" of expert pathologists, technologists, and scientists, representing academic centers, reference laboratories, and various agencies, issued standardization testing recommendations, aimed at optimization of clinical ER testing environment, as a step toward improved standardized testing.
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 | 0.082 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.014 | 0.004 |
| Research integrity | 0.017 | 0.017 |
| 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".