Formation des médecins généralistes à la dermoscopie : revue systématique de la littérature
Bibliographic record
Abstract
Introduction : Dermoscopy is a non-invasive skin examination technique using a dermoscope that improves early melanoma diagnosis and the evaluation of skin lesions in primary care. Despite its proven effectiveness, its use by general practitioners (GPs) remains limited, mainly due to a lack of appropriate training. This study aimed to provide an overview of dermoscopy training programs for general practitioners described in the literature. Materials and Methods : A systematic literature review was conducted using PubMed and Cochrane databases. Included studies described dermoscopy training for GPs or GP trainees. Literature reviews, studies focusing solely on teledermoscopy, artificial intelligence, or specific conditions were excluded. Methodological quality was assessed using the Cochrane Risk of Bias tool (for randomized controlled trials) and the Newcastle-Ottawa Scale (for observational studies). Result s: Eighteen studies were included. Six main themes emerged: training content, format, duration, outcome measures, timing of evaluation, and prior dermoscopy experience. Dermoscopy training improved knowledge, confidence, and the clinical impact of general practitioners. All studies reported post-training improvement. Discussion : This literature review shows that dermoscopy training, regardless of its format, improves diagnostic skills and confidence among GPs. Some studies reported reductions in unnecessary biopsies and specialist referrals. However, limitations included selection bias, lack of long-term evaluation, and no studies from France. The findings suggest that structured training—combining theory, clinical cases, and simple visual tools—could be beneficial. Consideration should be given to integrating such training into the curriculum of general practice residents, using a progressive format adapted to their clinical context.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".