Optimizing technique in elliptical excisional surgery: some pearls for practice
Bibliographic record
Abstract
Conflicts of interest: none declared. Sir, Proficient technique when performing classic elliptical (fusiform) excisions is a fundamental skill required by dermatologists. The importance of correct design of an ellipse (which most commonly bears a 3 : 1 length to width ratio with a 30° angle at the apices) is emphasized in most if not all basic dermatological surgical texts. When designed and performed correctly, closure of the elliptical defect forms a smooth linear suture line, with no standing cutaneous deformities. Having taught introductory surgical skills to a large number of dermatology trainees, general practitioners and specialist nurses, we have found a number of practical ‘surgical pearls’ relating to this type of excision to be of benefit in optimizing the outcome of elliptical excisional surgery. Prior to removing a lesion, it is our standard practice to mark the visible clinical extent of the lesion meticulously in good light using magnification (Fig. 1a). This pivotal step may often be neglected. A dotted line is placed around the periphery of the lesion, and a continuous line marked outside this to delineate the required margins, most commonly 4 mm for excision of the majority of nonmelanoma skin cancers on the trunk and limbs1 (Fig. 1b). A routine such as this encourages a clear definition of excision margins and enables both the operator and patient to appreciate fully the size of the defect that will be closed and subsequently the length of the resultant scar. We have demonstrated by serial auditing that such a routine greatly reduces the rate of incomplete excision of lesions, independent of the level of expertise of the operator.
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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 | 0.042 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".