Basal Cell Carcinoma Excision Guided by Dermoscopy: A Retrospective Study in Macau
Bibliographic record
Abstract
Objective: Dermoscopic evaluation of tumor margins may help to accurately detect lateral borders before surgical excision. The purpose of this study was to comprehensively evaluate the usefulness of dermoscopically detecting basal cell carcinoma tumor margins before surgical excision. Methods: The authors retrospectively analyzed the outcomes of 60 basal cell carcinomas that were excised after undergoing dermoscopic evaluation of the margins from 2016 to 2018 in a single center in Macau SAR, China. Descriptive statistical analysis was carried out by using frequencies and percentages. Results: All treated tumors were completely excised, although five had safety margins of <1 mm. No re-excisions were performed and during a medium follow-up period of 31 months, none of the 60 tumors showed any evidence of recurrence. Conclusions: Our data suggest that the preoperative dermoscopic evaluation of tumor margins increases the chances of successful and complete tumor excision, while preserving healthy adjacent tissue. Further studies comparing dermoscopy-assisted versus conventional excision are needed to better evaluate the value of this technique.
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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 | 0.000 |
| 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".