Dermoscopy Overview and Extradiagnostic Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
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 it