Overtreatment of dysplastic naevi: results of a multiregional UK questionnaire study
Bibliographic record
Abstract
Dear Editor, Dysplastic naevus is frequently encountered in clinical practice, but its definition, gradation and relationship to melanoma risk remains controversial.1 Surveys from Australia, Canada and the USA indicate variation in management between clinicians.2–4 Traditionally, dysplastic naevi have been categorized according to architectural and cytological features as mildly, moderately or severely dysplastic, but a recent two-grade system has reclassified lesions formerly termed mildly dysplastic as benign, and naevi with formerly moderate and severe dysplasia as low-grade and high-grade, respectively.5 The World Health Organization 2023 classification recommends no re-excision for histologically margin-positive mildly dysplastic naevi; for margin-positive moderately dysplastic naevi (low-grade), re-excision can be considered; incompletely excised severely dysplastic naevi (high-grade) should be re-excised in view of features that may overlap with melanoma in situ. Similarly, the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) version 2.0 recommends that all high-grade dysplastic naevi with involved margins be re-excised.6 However, excised dysplastic naevi with involved histological margins have not been shown to progress to melanoma.7
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".