Spindle cell melanocytic lesions: part II—an approach to intradermal proliferations and horizontally oriented lesions
Bibliographic record
Abstract
Melanocytic lesions show great morphological diversity in their architecture and the cytomorphological appearance of their composite cells. Whereas functional melanocytes show a dendritic cytomorphology and territorial isolation, lesional nevomelanocytes and melanoma cells typically show epithelioid, spindled or mixed cytomorphologies, and a range of architectural arrangements. Spindling is common to melanocytic lesions, and may either be a characteristic feature or a divergent appearance. The presence of spindle cells may mask the melanocytic nature of a lesion, and is often disconcerting, either due to its infrequent appearance in a particular lesion or its interpretation as a dedifferentiated phenotype. Spindle cell melanocytic lesions follow the full spectrum of potential biological outcomes, and difficulty may be experienced judging the nature of a lesion due to a lack of consistently reliable features to predict biological behaviour. Over time, recognition of numerous histomorphological features that may portend a more aggressive lesion have been identified; however, the translation of these features into a diagnostic entity requires a gestalt approach. Although most spindle cell melanocytic lesions may reliably be resolved through this standard approach, problem areas do exist for the surgical pathologist or dermatopathologist. With this review (part II of II), we complete our discussion of spindle cell melanocytic lesions, in order to: (1) model a systematic approach to such lesions; and (2) provide familiarity with those melanocytic lesions which either typically or occasionally display a spindled cytomorphology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".