Variable fidelity of tissue‐marking dyes in surgical pathology
Bibliographic record
Abstract
AIMS: Pathology specimens often contain important margins that must be identified from gross examination of specimens through to microscopic examination. Commonly, unique colours of tissue-marking dye (TMD) are applied to each margin, which facilitates both macroscopic and microscopic identification. Various techniques have been described, but the colour endurance and fidelity of TMDs following special tissue processing have not been addressed. The aim of this study was to evaluate the performance of various TMDs through decalcification and immunohistochemistry (IHC) protocols. METHODS AND RESULTS: Samples of TMDs from two manufacturers and acrylic artists' inks were obtained in seven colours and applied to excess non-diagnostic surgical pathology tissue. Tissues were subjected to a decalcification protocol or directly processed in a routine fashion. The presence and colour of TMD or ink were assessed on routine H&E sections and following IHC. Of the colours that reliably survived routine processing, loss of colour and colour change following decalcification and IHC protocols were seen with one manufacturer's product. CONCLUSIONS: TMD may lose or change its colour during special tissue processing. This previously unreported artefact may lead to potentially serious errors in margin assessment and reporting. Laboratories should evaluate TMDs and inks through routine processing, decalcification, and IHC protocols, to ensure colour endurance and fidelity.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".