An Audit of Failed Immunohistochemical Slides in a Clinical Laboratory: The Role of On-Slide Controls
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Bibliographic record
Abstract
Appropriate controls are critical for the correct interpretation of immunohistochemistry (IHC) assays and help to detect unsuccessful/suboptimal slides. We performed an audit of slides that were designated as being "failed" by the IHC laboratory (ie, laboratory-failed slides) of a large North American oncology and transplant center. All slides were run with on-slide controls. The study included analysis of only those failed slides where staining of both internal and external controls were unsuccessful/suboptimal in a period of 65 days. Failed slides were categorized based on the reason why the laboratory failed the slides. The study compared frequencies of failed slides across 9 automated stainers from 2 manufacturers and between class 1 and class 2 biomarkers. Distinction between "failed slides" and "false-negative/false-positive tests" is emphasized. The study included 22,234 IHC slides in the study period. Of those, 452 (2%) were designated as "failed" by the laboratory. Class 1 and class 2 tests showed failure rates of 0.8% and 9%, respectively. The most frequent reason for failed slides on one platform related to "no or weak staining," whereas the other had more failed slides due to "high signal-to-noise ratio" (P<0.0001, χ test). Although the slides were run in groups of the same as well as different IHC protocols, unsuccessful/suboptimal testing typically manifested as individual slides (92%) and not as groups of slides; this indicates that so-called "batch controls" are not suitable as controls for automated platforms. We conclude that in the era of automated IHC staining platforms, on-slide controls allow for the proper identification of IHC slides that should be failed by the IHC laboratory and represent a powerful tool for preventing the reporting of false-negative/false-positive tests.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it