Histopathologic Excision Margin Affects Local Recurrence Rate
Bibliographic record
Abstract
OBJECTIVE: Prospective trials have shown that 1-cm and 2-cm margins are safe for melanomas <1 mm thick and > or =1 mm thick, respectively. It is unknown whether narrower margins increase the risk of LR or mortality. SUMMARY BACKGROUND DATA: To determine the relationship between histopathologic excision margin, local recurrence (LR) and survival for patients with melanomas < or =2 mm thick. METHODS: Data were extracted from the Sydney Melanoma Unit database for all patients with cutaneous melanoma < or =2 mm thick, diagnosed up to 1996. Patients with positive excision margins or follow-up <12 months were excluded, leaving 2681 for analysis. Outcome measures were LR (recurrence <5 cm from the excision scar), in-transit recurrence, and disease-specific survival. Factors predicting LR and overall survival were tested with Cox proportional hazards analysis. RESULTS: Median follow-up was 83.8 months. LR was identified in 55 patients (median time to recurrence, 37 months). At 120 months, the actuarial LR rate was 2.9%. Five-year survival after LR was 52.8%. In multivariate analysis, only margin of excision and tumor thickness were predictive of LR (both P = 0.003). When all patients with a margin <0.8 cm in fixed tissue (corresponding to a margin of <1 cm in vivo) were excluded from analysis, margin was no longer significant in predicting LR. Thickness, ulceration, and site were predictive of survival, but margin was not (P = 0.49). CONCLUSIONS: Histopathologic margin affects the risk of LR. However, if the in vivo margin is > or =1 cm, it no longer predicts risk of LR. Patient survival is not affected by margin.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".