American Society of Clinical Oncology/College ofAmerican Pathologists guideline recommendations forimmunohistochemical testing of estrogen andprogesterone receptors in breast cancer
Bibliographic record
Abstract
Purpose: To develop a guideline to improve theaccuracy of immunohistochemical (IHC) estrogen receptor(ER) and progesterone receptor (PgR) testing in breastcancer and the utility of these receptors as predictivemarkers.Methods: The American Society of Clinical Oncologyand the College of American Pathologists convened aninternational Expert Panel that conducted a systematicreview and evaluation of the literature in partnership withCancer Care Ontario and developed recommendations foroptimal IHC ER/PgR testing performance.Results: Up to 20% of current IHC determinations ofER and PgR testing worldwide may be inaccurate (falsenegative or false positive). Most of the issues with testinghave occurred because of variation in preanalyticvariables, thresholds for positivity, and interpretationcriteria.Recommendations: The Panel recommends that ER andPgR status be determined on all invasive breast cancers andbreast cancer recurrences. A testing algorithm that relieson accurate, reproducible assay performance is proposed.Elements to reliably reduce assay variation are specified. It is recommended that ER and PgR assays be consideredpositive if there are at least 1% positive tumor nuclei in the sample on testing in the presence of expected reactivity of internal (normal epithelial elements) and external controls. The absence of benefit from endocrine therapy for women with ER-negative invasive breast cancers has been confirmed in large overviews of randomized clinical trials.(Arch Pathol Lab Med. 2010;134:907–922)
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".