The Laboratory Score/Reference Method Score Ratio (LSRSR) Is a Novel Tool for Monitoring Laboratory Performance in Immunohistochemistry Proficiency Testing of Hormone Receptors in Breast Cancer
Bibliographic record
Abstract
Canadian Immunohistochemistry Quality Control (CIQC) operates an academic proficiency testing (PT) program using a traditional expert panel-based qualitative assessment system. The image analysis approach is increasingly considered for use in PT to follow demand for precision in immunohistochemical test calibration. CIQC introduces and explores the usefulness of a novel image analysis-based tool, the laboratory score/reference method score ratio (LSRSR) for PT. Two CIQC runs with 33 and 57 participants, respectively, were analyzed for interlaboratory concordance for estrogen receptor results using expert panel-based and LSRSR systems. Samples included tissue microarrays with 40 tissue cores each. The LSRSR was calculated from participants' and reference laboratory H scores measured by image analysis. We found lower concordance with reference method results for participating laboratories by LSRSR than those reported by the expert panel; although the expert panel observed those differences, it was not able to measure them without LSRSR. LSRSR may be useful in monitoring laboratory performance for quantitative immunohistochemical testing.
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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 | 0.034 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".