Fresh Cut Versus Stored Cut Paraffin-embedded Tissue: Effect on Immunohistochemical Staining for Common Breast Cancer Markers
Bibliographic record
Abstract
The proper handling of unstained paraffin slides for immunohistochemistry has been a matter of debate, with several studies demonstrating loss of antigenicity with prolonged storage at room temperature, 4°C and -20°C. The purpose of this study was to determine whether long-term storage of unstained slides at -80°C would impact the staining intensity and expression distribution of markers used to molecularly subtype breast cancer specimens [estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), cytokeratin 5 (CK5), epidermal growth factor receptor (EGFR), and Ki67]. The staining pattern of previously unstained breast tumor slides (n=39 to 64) stored at -80°C for a minimum of 9.93 years (avg., 12.8 y) was compared with the staining pattern of fresh cut slides from the same tumors. The Allred scoring method was used to score ER (0 to 2, negative; 3 to 8, positive), CK5 (≥4, positive), and EGFR (≥4, positive). ASCO/CAP guidelines were used to assess HER2 (0/1+, 2+, or 3+). Ki67 scores were determined based on the proportion of cells stained of any intensity, with 20% staining used as a cut-off. Agreement was assessed using concordance rates and chance-corrected agreement statistics. The chance-corrected agreements were as follows: 0.94 (38/39) for ER, 0.92 (53/55) for CK5, 0.87 (61/64) for EGFR, 0.86 (37/39) for HER2, and 0.67 (46/54) for Ki67. Long-term storage of cut unstained slides at -80°C does not significantly impact the scoring interpretation of ER, CK5, EGFR, and HER2.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".