Open Versus Endoscopic Approach for Sinonasal Melanoma: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Open resection (OR) of sinonasal mucosal melanoma (SNM) traditionally has been the gold standard for treatment. However, endoscopic resection (ER) has recently become a surgical alternative. The aim of this study was to compare survival outcomes between OR and ER of SNM. METHODS: A literature search encompassing PubMed, Embase, Cochrane Library, Web of Science, ClinicalTrials.gov, and Google Scholar was performed. Two reviewers independently screened for original studies comparing survival outcomes between OR and ER for SNM. Data were systematically collected on study design, patient demographics, outcomes, and level of evidence. Quality assessment was performed using the Newcastle-Ottawa scale (NOS). Meta-analysis of overall survival and disease-free survival was performed using random-effects models. RESULTS: The initial search yielded 2078 abstracts, of which 9 cohort studies were included for a total of 510 patients from 6 different countries. The average quality of all included studies using the NOS was 7.7 stars. Six out of 7 studies reported no differences in the stages of SNM between patients receiving ER versus OR. Overall survival was longer in the ER group versus OR group (hazard ratio [HR]: 0.68, 95% confidence interval [CI]: 0.49-0.95). There was no significant difference in disease-free survival between groups (HR: 0.59, 95% CI: 0.28-1.25). CONCLUSION: Based on the available literature, an endoscopic approach for SNM resection has survival outcomes that are similar or greater compared to an open approach.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.005 | 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".