Recognition and Discrimination of Tissue-Marking Dye Color by Surgical Pathologists
Bibliographic record
Abstract
OBJECTIVES: A variety of tissue-marking dye (TMD) colors can be used to indicate surgical pathology specimen margins; however, the ability of pathologists to differentiate between specific microscopic margin colors has not been assessed systematically. This study aimed to evaluate pathologists' accuracy in identifying TMD color and determine the least ambiguous combinations of colors for use in surgical pathology. METHODS: Seven colors of TMD were obtained from three manufacturers and applied to excess formalin-fixed uterine tissue. Study blocks contained multiple tissue pieces, each marked with a different color from the same manufacturer. Slides were assessed by eight participants for color and color distinctness of each piece of tissue. RESULTS: Black, green, red, and blue TMDs were accurately identified by most participants, but participants had difficulty identifying violet, orange, and yellow TMDs. Black, green, and blue TMDs were most commonly rated as "confidently discernable." CONCLUSIONS: Pathologists have difficulty identifying and distinguishing certain colors of TMDs. The combined use of certain colors of TMDs (yellow/orange/red, blue/violet, and red/violet) within the same specimen should be avoided to decrease the risk of inaccurately reporting specimen margins.
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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 | 0.018 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".