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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it