Inappropriate calibration and optimisation of pan-keratin (pan-CK) and low molecular weight keratin (LMWCK) immunohistochemistry tests: Canadian Immunohistochemistry Quality Control (CIQC) experience
Bibliographic record
Abstract
AIMS: Pan-cytokeratin (pan-CK) and low molecular weight cytokeratin (LMWCK) tests are the most common immunohistochemistry (IHC) tests used to support evidence of epithelial differentiation. Canadian Immunohistochemistry Quality Control (CIQC), a new provider of proficiency testing for Canadian clinical IHC laboratories, has evaluated the performance of Canadian IHC laboratories in two proficiency testing challenges for both pan-CK and LMWCK. METHODS: CIQC has designed a 70-sample tissue microarray (TMA) for challenge 1 and a 30-sample TMA for challenge 2. There were 13 participants in challenge 1, and 62 in challenge 2. All results were evaluated and scored by CIQC assessors and compared with reference laboratory results. RESULTS: Participating laboratories often produced false-negative results that ranged from 20% to 80%. False-positive results were also detected. About half of participating clinical laboratories have inappropriately calibrated IHC tests for pan-CK and LMWCK, which are the most commonly used markers for demonstration of epithelial differentiation. The great majority of laboratories were not aware of the problem with calibration of pan-CK and LMWCK tests because of inappropriate selection of external positive controls and samples for optimisation of these tests. Benign liver and kidney are the most important tissues to include as positive controls for both pan-CK and LMWCK. CONCLUSIONS: Participation in external quality assurance is important for peer comparison and proper calibration of IHC tests, which is also helpful for appropriate selection of positive control material and material for optimisation of the tests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.081 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".