Melanoma in Primary Care: A Narrative Review of Training Interventions and the Role of Telemedicine in Medical Education
Bibliographic record
Abstract
General practitioners play a crucial role in the early detection and prevention of cutaneous melanoma. However, structured training on skin cancer diagnosis and management is often lacking. This narrative review aims to map the current educational interventions for general practitioners focused on melanoma, assess their methodological approaches and outcomes, and explore the contribution of e-learning and telemedicine in medical education. A comprehensive literature search identified 54 relevant studies published between 1 January 1995 and 31 December 2024. Data were extracted and categorized by topics covered, training methodology, interactivity, and clinical outcomes. Training programs varied widely in duration, delivery, and content. Interventions that integrated dermoscopy and interactive methodologies demonstrated improved diagnostic accuracy and clinical impact. E-learning, particularly asynchronous models, emerged as a flexible and effective modality, although few studies evaluated long-term retention or clinical practice changes. Educational programs tailored to general practitioners and enriched with dermoscopy and telemedicine tools show promise in improving melanoma detection and care. Structured, interactive, and blended/hybrid learning models should be prioritized to support effective primary and secondary prevention.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".