Mohs Micrographic Surgery Is Equivalent to Nail Unit Excision or Amputation for Melanoma In Situ of the Nail Unit: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Local recurrence (LR) rates of melanoma in situ (MIS) of the nail apparatus treated with different surgical modalities are unknown. OBJECTIVE: To evaluate the differences in LR rates of nail apparatus MIS treated with Mohs micrographic surgery (MMS) versus nail unit excision (NUE) versus amputation. METHODS: Studies of nail/subungual MIS treated with MMS, NUE, or amputation were identified through multiple literature databases, including PubMed, MEDLINE, Embase, Web of Science, and Cochrane Library. Pooled data were assessed through meta-analyses and Fisher exact test. RESULTS: Of 280 studies identified, 20 met inclusion criteria (7 comparative studies and 13 single-arm studies). Among the 7 comparative studies, the LR was 4.38% (5/114) after NUE and 2.94% (1/34) after amputation (odds ratio: 0.937; 95% CI: 0.237-3.703). In the 13 noncomparative studies, 23 patients underwent MMS (pooled LR estimate: 11.07%; 95% CI: 3.22%-31.81%) and 140 patients underwent NUE (pooled LR estimate:8.04%, 95% CI: 4.43%-14.16%). The difference in LR rate between MMS, NUE, and amputation was not statistically significant ( p = .578). CONCLUSION: Local recurrence of nail/subungual MIS in cases treated with MMS was not statistically different than in cases treated with NUE and was comparable to amputation. Further studies investigating the use of MMS for the treatment of nail/subungual MIS are warranted.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".