Academic and Nonacademic Laboratories Perform Equally on CIQC Immunohistochemistry Proficiency Testing
Bibliographic record
Abstract
OBJECTIVES: To test whether academic centers (ACs) are more successful than nonacademic centers (NACs) in immunohistochemistry (IHC) external quality assessment challenges in the Canadian Immunohistochemistry Quality Control (CIQC) program. METHODS: Results of 9 CIQC challenges for breast cancer marker (BM) and various non-breast cancer marker (NBM) tests were examined. Success rates were compared between AC/NAC laboratories and those located in small or large cities. Performance was also correlated with annual IHC case volumes. RESULTS: There was no statistically significant difference in performance in any of the comparisons. However, overall performance on BM was significantly better (P < .0001, t test) than on NBM tests regardless of AC/NAC nature or city size. The mean failure rate on NBM was approximately twice that of BM tests. CONCLUSION: Our results suggest that recent emphasis on breast hormone IHC quality assurance has led to improved test quality.
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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 | 0.057 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".