An Audit of Failed Immunohistochemical Slides in a Clinical Laboratory: The Role of On-Slide Controls
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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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 | 0.062 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".