Prognostic Relevance of Type 2 Diabetes and Metformin Treatment in Head and Neck Melanoma: Results from a Population-Based Cohort Study
Bibliographic record
Abstract
Background: Type 2 Diabetes (DM2) and the consecutively daily use of antidiabetic medication are characterized by a frequent prevalence worldwide and were shown to impact the initiation and progression of malignant diseases. While these effects were observed in a variety of malignancies, comprehensive data about the role of DM2 and antidiabetic drugs in the outcome of head and neck melanoma (HNM) patients are missing. Methods: This retrospective population-based cohort study included 382 HNM patients from Eastern Bavaria having received tumor resection to negative margins between 2010 and 2017. Recurrence-free survival (RFS) was evaluated with regard to DM2 and routine metformin intake. Statistical analysis was performed by uni- and multivariate analyses. The median follow-up time was 5.6 years. Results: DM2 was diagnosed in 68 patients (17.8%), routine metformin intake was found in 39 cases (10.2%). The univariate survival analysis revealed impaired 5-year RFS in HNM patients with DM2 compared to non-diabetic controls (p = 0.016; 64.0% and 74.5%, respectively). The multivariate Cox regression substantiated this effect (HR = 1.980, 95% CI = 1.108–3.538, p = 0.021). In detail, the cumulative locoregional recurrence rate displayed the most far-reaching negative effect on the RFS of diabetic HNM patients (HR = 4.173, 95% CI = 1.628–10.697, p = 0.003). For metformin intake, a profound positive effect on the RFS in multivariate statistics was observed, both in the complete cohort (HR = 0.396, 95% CI = 0.177–0.884, p = 0.024) as well as in the cohort of diabetic HNM patients (HR = 0.352, 95% CI = 0.135–0.913, p = 0.032). Conclusions: This study emphasizes that DM2 is a relevant comorbid condition in HNM patients, impairing patient survival. Metformin intake was associated with a favorable outcome in HNM patients, providing possible therapeutic implications for future adjuvant treatment regimes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".