Immunohistochemistry of Breast Tumor Markers on Archived Bouin-fixed Paraffin-embedded Tissues
Bibliographic record
Abstract
Neutral-buffered formalin is the most commonly used tissue fixative in pathology laboratory. Among other fixatives, Bouin's solution has been used in several laboratories and is still in use for particular tissues. In this project, we determine if we can study breast clinical markers on archived Bouin-fixed tissue samples with immunohistochemistry (IHC) protocols optimized for tissue fixed in neutral-buffered formalin. To evaluate the concordance of IHC results between formalin-fixed and Bouin-fixed tissues, we calculated the concordance percentage and the κ statistic of 12 clinical IHC markers quantified by an automated system on breast cancer tissues fixed in neutral-buffered formalin and their corresponding tissues fixed in Bouin's solution. When positivity threshold of immunostaining was setup at ≥10% for both fixation conditions, we observed a concordance percentage of 83.9% (κ=0.65). However, when positivity threshold of immunostaining was lowered to 3% to 4% for Bouin-fixed tissues, concordance percentage was then of 96.8% (κ=0.92). Our data demonstrate that we can study IHC markers on archived Bouin-fixed tissue from patients with long clinical follow-up using IHC protocols optimized for formalin-fixed tissues after an adjustment of the positivity threshold of immunostaining quantified by an automated system.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".