Sentinel lymph-node biopsy for melanoma of the trunk and extremities: the McGill experience.
Bibliographic record
Abstract
OBJECTIVE: To determine the effectiveness of sentinel lymph-node (SLN) biopsy for melanoma of the trunk and extremities. DESIGN: Case series review. SETTING: Royal Victoria Hospital, a Canadian university hospital. PATIENTS: Thirty-six patients (18 women and 18 men) seen between October 1996 and December 1998 with melanoma 1 mm or more in thickness with clinically negative lymph-node basins. Follow-up was 396 days. INTERVENTIONS: SLN biopsy. Technetium-99m filtered sulfur colloid (0.5 mCi) was injected intradermally around the melanoma or the excision scar 10 to 15 minutes before the surgical skin preparation. The identification of the SLN(s) was done with a hand-held gamma probe. Local anesthesia was used mostly for inguinal SLN biopsy whereas general anesthesia was usually required for axillary SLN biopsy. Preoperative lymphoscintigraphy was used only for trunk melanomas. OUTCOME MEASURES: Morbidity, successful identification of the sentinel node and locoregional recurrence. RESULTS: The mean age of patients at diagnosis was 53.4 years (range from 22-76 yr). The melanomas were distributed between the lower extremities (20 patients), upper extremities (8 patients) and trunk (8 patients). The mean Breslow thickness was 2.35 mm (range from 1-8 mm). Lymphoscintigraphy accurately localized the lymph-node drainage basin for trunk melanomas. In 1 patient the SLN could not be identified because the radiocolloid failed to migrate (failure rate 2.8%). The average number of SLNs removed was 1.97. Eight patients (22%) had sentinel nodes positive for malignant disease. The postoperative complication rate was 8.5%. Seven of 8 patients with positive SLNs underwent a complete node dissection (1 patient refused). Of the completion dissections only 2 patients had positive non-SLNs. All patients with positive nodes received interferon alpha-2b as adjuvant treatment. At follow-up, 34 patients are alive with no evidence of disease, 1 patient with a positive SLN is alive with distant metastatic disease and 1 patient with a negative SLN is dead of disseminated disease. CONCLUSION: SLN biopsy is a feasible technique with an acceptable failure rate and is thus a useful tool in the surgical management of melanoma.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".