Dermoscopy Overview and Extradiagnostic Applications
Bibliographic record
Abstract
Cutaneous diagnosis is often, but not always, visually based. Dermatologists tend to encounter situations where the possibility of multiple differentials complicates the diagnosis and mandates investigations for confirmation. Methods commonly employed for cutaneous diagnosis may be invasive (skin and scalp biopsy), semi-invasive (slit skin smears, trichogram, etc.) or non-invasive (e.g., KOH smear, nail clipping, hair count for hair loss). Dermoscopy, also known as epiluminescence microscopy, or skin surface microscopy, is a non-invasive, in-vivo technique, which has traditionally found use in the evaluation and differentiation of suspicious melanocytic lesions from dysplastic lesions and melanomas, as well as keratinocyte skin cancers such as basal cell carcinoma (BCC) and squamous cell carcinoma (SCC).Over the last several years, the use of dermoscopy has been increasing in the context of general dermatological disorders including inflammatory dermatosis, pigmentary dermatosis, infectious dermatosis, and disorders of the hair, scalp, and nails. Some terms are used to describe specific indications: pigmentaroscopy for pigmented lesions, trichoscopy of the scalp and hair, onychoscopy of the nails, inflammoscopy for inflammatory dermatosis and lesions, as well as entomodermoscopy of skin infestations and infections . The role of dermoscopy in diagnosing disorders of general dermatology has undergone elaborate discussion . In this chapter, we shall review the plethora of extra-diagnostic indications of this technique and highlight technical aspects worth considering.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".