Patterns of Failure in Cutaneous Head and Neck Melanoma Following Negative Sentinel Lymph Node Biopsy: A Retrospective Cohort Study
Bibliographic record
Abstract
Background Cutaneous head and neck melanoma (cHNM) has a high rate of false-negative sentinel lymph node biopsy (SLNB) and up to a 25% risk of recurrence despite negative SLNB. The aim of this study was to investigate the pattern of melanoma recurrence in patients with cHNM with negative SLNB. Methods A retrospective cohort study of consecutive cHNM patients at a tertiary care centre from 2014-2022. We included all cHNM patients with negative SLNB. All patients were categorized into Breslow thickness >2 mm and ≤2 mm and extracted information pertaining to histopathological characteristics and the presence and type of disease recurrences. We performed multivariable analysis using logistic and cox regression. We used an alpha of 0.05 and all statistical analyses were performed using R software. Results Overall, 167 patients met eligibility criteria and of these, 53.5% patients had cHNM ≤2 mm thick and 46.7% had lesions >2 mm thick. The overall recurrence rate was 29.3%. Multivariable analysis demonstrated that Breslow thickness [aOR: 5.89 (95% CI: 1.37, 32.3), P = 0.02] was associated with distant recurrence. Multivariable cox regression also identified that pathological ulceration [aHR: 3.17 (95% CI: 1.61, 7.66), P = 0.01] predicted time to distant recurrence. The SLNB false omission rate was 3.6% (95% CI: 1.3%, 7.7%). Conclusion SLNB-negative cHNM patients with high-risk pathological features may benefit from adjuvant immunotherapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".