Assessment of Interlaboratory Variation in the Immunohistochemical Determination of Estrogen Receptor Status Using a Breast Cancer Tissue Microarray
Bibliographic record
Abstract
The determination of tumor cell estrogen receptor (ER) expression status by immunohistochemical analysis has become standard practice, yet assay reproducibility has not been assessed adequately. By using a breast cancer tissue microarray, we examined interlaboratory variability in ER reporting. A 2-fold redundant tissue microarray block was created from 29 breast cancers. Unstained slides were distributed to 5 laboratories, and each laboratory immunostained and scored 1 slide for ER. Interlaboratory agreement ranged from moderate to high (overall kappa = 0.54 for 0-3+ grading; overall kappa = 0.84 for negative vs positive assessment of ER status). When 1 observer scored each of the 5 slides, interlaboratory agreement was slightly better (kappa = 0.63 for 0-3+ scoring; kappa = 0.96 for negative vs positive scoring). One laboratory, which had used an antibody and antigen retrieval method different from the others, demonstrated only fair concordance with the other 4 laboratories, but there was substantial intralaboratory interobserver agreement and excellent agreement with an outside observer reviewing the slide stained in that laboratory. The tissue microarray was an efficient and effective tool for identifying variability in ER reporting and should prove valuable in other external quality assurance programs.
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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 | 0.068 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".