The perimeter technique for lentigo maligna: an alternative to Mohs micrographic surgery
Bibliographic record
Abstract
BACKGROUND: Lentigo maligna (LM) presents a challenge for complete surgical excision because of its extensive subclinical spread and predilection for the face. OBJECTIVE: To report our experience using the staged perimeter technique as an alternative to Mohs micrographic surgery for treatment of LM. METHODS: The perimeter procedure was performed on 11 patients with LM between March 2003 and June 2004. Data on patient and lesion characteristics, number of stages required to obtain clear margins, and follow-up was obtained by chart review. RESULTS: A mean of 1.9 stages were required to achieve clear margins. A mean of 7 tissue specimens were sent to pathology per patient for evaluation. After a mean follow-up of 4.7 months, all patients were free of recurrence. CONCLUSIONS: The perimeter technique is a simple method of margin-controlled excision of LM. The main advantage is that all margins are examined with permanent sections. The main drawback is that multiple operative sessions are required to complete the procedure. This technique does not require specific Mohs training and is therefore applicable to non-Mohs surgeons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".