A Consortium for Analytic Standardization in Immunohistochemistry
Bibliographic record
Abstract
CONTEXT.—: The authors announce the launch of the Consortium for Analytic Standardization in Immunohistochemistry, funded with a grant from the National Cancer Institute. As with other laboratory testing, analytic standards are important for many different stakeholders: commercial vendors of instruments and reagents, biopharmaceutical firms, pathologists, scientists, clinical laboratories, external quality assurance organizations, and regulatory bodies. Analytic standards are customarily central to assay development, validation, and method transfer into routine assays and are critical quality assurance tools. OBJECTIVE.—: To improve immunohistochemistry (IHC) test accuracy and reproducibility by integrating analytic standards into routine practice. To accomplish this mission, the consortium has 2 mandates: (1) to experimentally determine analytic sensitivity thresholds (lower and upper limits of detection) for selected IHC assays, and (2) to inform IHC stakeholders of what analytic standards are, why they are important, and how and for what purpose they are used. The consortium will then publish the data and offer analytic sensitivity recommendations where appropriate. These mandates will be conducted in collaboration and coordination with clinical laboratories, external quality assurance programs, and pathology organizations. DATA SOURCES.—: Literature review and published external quality assurance data. CONCLUSIONS.—: Integration of analytic standards is expected to (1) harmonize and standardize IHC assays; (2) improve IHC test accuracy and reproducibility, both within and between laboratories; and (3) dramatically simplify and improve methodology transfer for new IHC protocols from published literature or clinical trials to clinical IHC laboratories.
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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 | 0.309 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".