Paraffin-embedded Tissue Fragment Suspension (PETFS): A Novel Method for Quality Control Preparation in Immunohistochemistry
Bibliographic record
Abstract
Immunohistochemistry (IHC) is one of the most important adjunctive techniques in surgical pathology. Quality controls are essential for staining interpretation. The most common controls are cut from the formalin-fixed, paraffin-embedded tissue blocks in advance. In contrast, we developed paraffin-embedded tissue fragment suspension (PETFS), a novel method in liquid form, for quality control preparation. The liquid form controls were cut from the donor formalin-fixed, paraffin-embedded paraffin blocks, stored in the 4°C fridge easily, and added to the top and bottom of the test slide directly by pipetting. The tissue fragments from the PETFS had a comparable IHC staining pattern to that of the control sections from the original donor blocks. Over a 180-day testing period, the IHC staining pattern and intensity remained strong and specific. The clinical value of PETFS method was further validated by their successful application as controls for the expression of estrogen receptor, progesterone receptor, and C-erbB-2 in 240 breast invasive ductal carcinomas. We concluded that PETFS is a fast, low-cost, and less donor tissue consumption robust technique as quality controls for routine IHC staining in surgical pathologic practice.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".