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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".