Survival Benefit of Metformin as an Adjuvant Treatment for Head and Neck Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Aims: The relationship between the efficacy of metformin and the prognosis of patients with head and neck cancer (HNC) was still unclear. This study aims to clarify the prognostic value of metformin treatment using meta-analysis. Methods: Studies related to HNC prognosis and metformin were searched in Cochrane Library, Embase, LILACS, MEDLINE and PubMed databases. A meta-analysis was performed to evaluate the association between metformin therapy and the prognosis of HNC on overall survival (OS), disease-free survival (DFS) and disease-specific survival (DSS) and whether article quality, comorbidities, age, region or smoking had an influence on the prognosis of metformin treatment. Pooled hazard ratio (HR) and 95% confidence interval (CI) were analyzed to assess the effect. Results: Eleven eligible studies involving 14,694 participants were included. Metformin increased the OS (HR = 0.87, 95% CI: 0.76–0.99), but failed on DFS (HR = 0.67, 95% CI: 0.40–1.09) or DSS (HR = 0.69, 95% CI: 0.41–1.14) in HNC patients. Subgroup analysis showed metformin was associated with improved OS (HR = 0.66, 95% CI: 0.49–0.88), DFS (HR = 0.49, 95% CI: 0.26–0.92) and DSS (HR = 0.38, 95% CI: 0.22–0.65) in studies with higher Newcastle-Ottawa Scale (NOS) scores. Subgroup analysis of age indicated that patients younger than 65 years (OS, HR = 0.67, 95% CI: 0.49–0.92) were more likely to benefit from metformin treatment. Subgroup analysis of comorbidities showed metformin significantly improved patient outcomes in studies without adjusted for comorbidities (OS, HR = 0.66, 95% CI: 0.51–0.85; DSS, HR = 0.38, 95% CI: 0.22–0.65), but not in studies that adjusted for comorbidities. Conclusions: Metformin improved the prognosis of HNC patients as an adjuvant therapy, especially in those with higher NOS scores. Age and comorbidities of HNC patients influenced the therapeutic effect of metformin. Further well-conducted investigations are needed.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.039 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".