Bibliographic record
Abstract
Early detection of skin cancer allows timely treatment and improves clinical outcome. The armamentarium for diagnosing skin cancer has been growing notably over the last decades. New tools have led to earlier recognition and a more specific and sensitive diagnosis. In this editorial, we discuss several recent studies published in the BJD on the diagnosis of pigmented skin lesions. The majority of studies published on the detection of skin cancer have investigated methods that are already widely accepted and increasingly used in dermatology, such as dermatoscopy, reflectance confocal microscopy (RCM) and teledermatology. Other investigators have walked off the beaten paths and reported unconventional findings, for example Willis et al. investigated a dog's olfactory ability to discriminate melanoma from control skin lesions.1 In this study, a Labrador, named Ronnie, performed 20 double‐blind tests, each requiring the selection of one melanoma from nine controls, consisting of three each of basal cell carcinomas, naevi and healthy skin. Ronnie correctly identified the melanoma on nine occasions (45%), vs. two expected by chance alone. This creative work demonstrates that invasive melanoma emits volatile organic compounds that differ from those of control lesions. The volatile compounds might be utilized as new biomarkers for a noninvasive diagnosis of melanoma using standardized biochemical assays.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".