Meta-analysis of completion lymph node dissection in sentinel lymph node-positive melanoma
Bibliographic record
Abstract
Abstract Background The role of completion lymph node dissection (CLND) in patients with sentinel lymph node (SLN)-positive melanoma continues to be debated. This systematic review and meta-analysis evaluated survival and recurrence rate in these patients who underwent CLND, compared with observation. Methods A comprehensive MEDLINE and Embase database search was performed for cohort studies and RCTs published between January 2000 and June 2017 that assessed the outcomes of CLND compared with observation in patients with SLN-positive melanoma. The primary outcome was survival and the secondary outcome was recurrence rate. Studies were assessed for quality using the Cochrane risk-of-bias tool for RCTs and Newcastle–Ottawa Scale for cohort studies. Pooled relative risk or hazard ratio with 95 per cent confidence intervals were calculated for each outcome. The extent of heterogeneity between studies was assessed with the I2 test. The protocol was registered in PROSPERO (CRD42017070152). Results Fifteen studies (13 cohort studies with 7868 patients and 2 RCTs with 2228 patients) were identified for qualitative synthesis. Thirteen studies remained for quantitative meta-analysis. Survival was similar in patients who underwent CLND and those who were observed (risk ratio (RR) for death 0·85, 95 per cent c.i. 0·71 to 1·02). The recurrence rate was also similar (RR 0·91, 0·79 to 1·05). Conclusion Patients with SLN-positive melanoma do not have a significant benefit in survival or recurrence rate if they undergo CLND rather than observation.
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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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.048 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".