Implementation of a Canadian External Quality Assurance Program for Breast Cancer Biomarkers
Bibliographic record
Abstract
Immunohistochemistry results for estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 are used to guide breast carcinoma patient management and it is essential to monitor these tests in external quality assurance (EQA) programs. Canadian Immunohistochemistry Quality Control is a web-based program with novel approach to EQA. Canadian Immunohistochemistry Quality Control RUN2 included tissue microarray slides with 38 samples tested by 18 immunohistochemical laboratories. Deidentified results were posted for viewing at www.ciqc.ca including all used protocols matched with scanned slides for virtual microscopy and garrattograms. Sensitivity, specificity, Kendall W test (concordance between laboratories), and kappa statistics (agreement with designated reference values) were calculated. Kappa values were within the target range (>0.8, or "near perfect" agreement) for 85% results. Kendall coefficient was 0.942 for estrogen receptor, 0.930 for progesterone receptor, and 0.958 for human epidermal growth factor receptor 2. The anonymous participation, quick feedback, and unrestricted full access in EQA results provides rapid insight into technical or interpretive deficiencies, allowing appropriate corrective action to be taken whereas the use of tissue microarrays enables meaningful statistical analysis.
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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 | 0.039 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".