Dermoscopy, a useful tool for general practitioners in melanoma screening: a nationwide survey
Bibliographic record
Abstract
BACKGROUND: Dermoscopy improves diagnostic accuracy in melanoma, as shown by several meta-analyses. Although it is used by general practitioners (GPs) in Australia, Canada and Italy, no published data on this topic are available in France. OBJECTIVES: To review the opinions and use of dermoscopy by GPs in France and to understand their practice of skin examination. METHODS: We designed a descriptive and cross-sectional survey and conducted it between 26 November and 26 December 2014. An anonymous, multiple-choice questionnaire about the demographic characteristics, skin examination modalities and use and training in dermoscopy was sent to 4057 GPs in four large regions of France. Pearson, χ(2) , Student, Welch and Fisher tests were used for cross-tabulation statistical analysis. RESULTS: Only 8% of respondents had access to a dermoscope; most were male practitioners and aged > 50 years. Dermoscopy increased self-confidence in analysing pigmented lesions (P = 0·004), and dermoscopy users referred fewer patients to dermatologists. The number of biopsies was reduced in the dermoscopy users group (P = 0·004). In total, 425 questionnaires were returned and analysed. Dermoscopy users took more time to evaluate a single pigmented lesion (P = 0·015). Only 16·9% of physicians declared having received some training on dermoscopy, yet this number reached 47% for those owning a dermoscope. Their training was mostly short and recent. Overall 29·2% of the respondents said the main advantage was to reduce the number of referrals to the dermatologists (P = 0·004), while its main disadvantage was the necessity of training (54·6%). Our responders declared they could spend seven working days on a dermoscopy training course. CONCLUSIONS: Our study demonstrates positive opinions regarding dermoscopy, despite a minority of French GPs using this technique in the areas surveyed. The need for formal training appears to be the main limitation to wider use. Appropriate and specifically designed training programmes should be offered.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".