Impact of metformin on disease control and survival in patients with head and neck cancer: A retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: A number of in vitro and clinical studies have suggested potential antineoplastic effects of metformin. The impact of this medication on outcomes in head and neck cancer is less clear. Our aim was to determine the effect of metformin on outcomes within our large cohort of head and neck cancer patients with Type II Diabetes (T2DM). STUDY DESIGN: Retrospective cohort study. SETTING: Tertiary Cancer Centre. SUBJECTS AND METHODS: A retrospective review of individuals with head and neck squamous carcinoma (HNSCC) and T2DM between January 2005 and December 2011 at Princess Margaret Cancer Centre was conducted. Medication history was obtained from surveys at initial presentation and electronic medical record review. Using Cox regression analyses, the association between metformin use and local, regional and distant failures was explored. Subgroup analyses were conducted for oral cavity, oropharynx and larynx. RESULTS: A total of 329 HNSCC patients with T2DM were identified, including 195 metformin users and 134 non-metformin users. Patients were well-matched in terms of clinical, pathologic, and treatment factors. No difference in local, regional, or distant failure was observed between diabetic metformin users and diabetic non-metformin users for the entire cohort or within subgroup analysis for subsite. No difference between the two groups was observed for overall survival, recurrence-free survival, and disease-specific survival at 5 years. CONCLUSION: No association between metformin use and oncologic outcomes were observed in this large cohort of HNSCC patients. Multicenter, prospective studies may be needed to verify previous studies identifying a potential anti-neoplastic effect of this medication.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".