Assessing exposure to dermoscopy in plastic surgery training programs
Bibliographic record
Abstract
BACKGROUND: Dermoscopy is a noninvasive tool that improves the diagnostic accuracy of melanoma and other cutaneous malignancies; yet, it is not widely used by plastic surgeons, who commonly manage skin lesions. Thus, the purpose of this study was to explore current practice patterns and knowledge of dermoscopy among plastic surgeons and postgraduate plastic surgery trainees. Additionally, interest to establish a formal dermoscopy curriculum as part of plastic surgery residency training was evaluated. METHODS: An online electronic questionnaire was developed and distributed through email to practicing plastic surgeons and plastic surgery trainees at two Canadian universities. RESULTS: Of the 59 potential participants, 27 (46%) responded. While the majority of participants were familiar with dermoscopy (n = 26; 96%), only one respondent reported using dermoscopy in clinical practice. However, all respondents reported exposure to melanoma clinically (n = 26; one participant did not provide a response). A lack of training, along with lack of access to dermatoscopes, were the most frequently cited reasons for not using dermoscopy. Knowledge scores with regard to dermoscopic features were also low; coupled with a noted propensity toward diagnostic or excisional biopsy, whichcould raise the benign to malignant ratio. Overall, 89% (n = 24) of respondents expressed interest in dermoscopy training in plastic surgery postgraduate training. CONCLUSIONS: Few responding plastic surgeons or plastic surgery residents currently use dermoscopy in training or practice but are interested in formal dermoscopy training in residency.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".