Differentiating Neurotized Melanocytic Nevi From Neurofibromas Using Melan-A (MART-1) Immunohistochemical Stain
Bibliographic record
Abstract
CONTEXT: Neurotized melanocytic nevi and neurofibromas are common, benign cutaneous neoplasms. Usually they are histologically distinct from each other; however, neurotized melanocytic nevi and neurofibromas can be clinically and histologically similar. OBJECTIVE: To determine whether Melan-A (MART-1) immunohistochemical stain is sufficient to differentiate neurotized melanocytic nevi from neurofibromas. DESIGN: Forty-nine consecutive specimens of melanocytic nevi with neurotization and 49 specimens of neurofibromas were selected. We used antibodies against Melan-A, S100, and neurofilament protein. RESULTS: All of the melanocytic nevi showed Melan-A staining within the neurotized areas, with most of the areas staining strongly positive, whereas all the neurofibromas were completely absent of Melan-A stain. All of the nevi, including the neurotized areas, stained strongly and diffusely for S100, whereas all the neurofibromas showed a distinctive, sharp, wavy pattern of S100 staining. Neurofilament protein showed scattered staining of both melanocytic nevi and neurofibromas. CONCLUSIONS: Our data indicate that Melan-A immunohistochemical staining is helpful in differentiating neurotized melanocytic nevi from neurofibromas when distinction on histomorphology alone is difficult.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".