Small-diameter melanocytic lesions: morphological analysis by means of<i>in vivo</i>confocal microscopy
Bibliographic record
Abstract
BACKGROUND: Small-diameter melanocytic lesions represent a diagnostic challenge for clinicians, as they do not follow the ABCD rule for diagnosis and do not always display reliable histopathological criteria. OBJECTIVES: To analyse the confocal features of small-diameter lesions (naevi and melanomas with diameter ≤ 5 mm) to determine whether they show specific morphological criteria. METHODS: Twenty-four melanomas and 72 naevi were subjected to dermoscopic and confocal evaluation along with histopathology. Significant dermoscopic and confocal differences between melanomas and naevi were evaluated by means of the Pearson χ(2) test. Odds ratios and 95% confidence intervals were calculated for each parameter. Binary logistic regression was performed to identify the reflectance confocal microscopy (RCM) independently significant features for melanoma diagnosis. RESULTS: The seven-point checklist dermoscopic score was ≥ 3 in 22 melanomas and in 33 naevi. The combination of cells' pleomorphism and architectural disorder (i.e. nonspecific pattern or irregular junctional nests upon confocal examination) are the most striking criteria for consistent diagnosis of small melanoma. The presence of atypical cells, more than five atypical cells per mm(2) , and roundish atypical cells at the dermoepidermal junction showed the highest odds ratios. From logistic regression, the presence of at least five pagetoid cells per mm(2) , tangled lines within the epidermis, and atypical roundish cells at the dermoepidermal junction resulted in the three independent confocal parameters that characterized small melanomas. CONCLUSIONS: Small melanomas frequently reveal specific dermoscopic and confocal features. Moreover, the combination of dermoscopy and RCM can lead to a correct diagnosis of a number of naevi that share some morphological aspects with melanomas.
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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 | 0.001 |
| 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".