Training Primary Care Physicians in Dermoscopy for Skin Cancer Detection: a Scoping Review
Bibliographic record
Abstract
In many countries, patients with concerning skin lesions will first consult a primary care physician (PCP). Dermoscopy has an evidence base supporting its use in primary care for skin cancer detection, but need for training has been cited as a key barrier to its use. How PCPs train to use dermoscopy is unclear. A scoping literature review was carried out to examine what is known from the published literature about PCP training in dermoscopy. The methodological steps taken in this review followed those described by Arksey and O'Malley, as revised by Levac et al. Four electronic databases were searched for evidence published up to June 2018. Sixteen articles were identified for analysis, all published since 2000. Ten training programs were identified all of which addressed dermoscopy of pigmented skin lesions, among other topics. Ten articles reported on a range of outcomes measured after training and showed generally positive results in terms of improved diagnostic performance, although no meta-analysis was conducted. However, it was unclear whether trained PCPs continued to use dermoscopy after training. Observational questionnaire data revealed that many PCPs use dermoscopy in practice without any formal training. The literature generally supports the use of dermoscopy by PCPs, but it is unclear whether current training leads to long-term change in PCPs' use of dermoscopy in clinical practice. Understanding this problem, as well as exploring PCPs' training needs, is essential to develop training programs that will facilitate the uptake and use of dermoscopy in primary care.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".