The impact of metformin use on recurrence and cancer-specific survival in clinically localized high-risk renal cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Recent data suggest that metformin may have anti-neoplastic properties. We sought to determine what effect metformin had on recurrence and cancer-specific survival (CSS) rates of patients with clinically localized pT2 and pT3 renal cell carcinoma (RCC) following radical or partial nephrectomy. METHODS: We obtained data on 784 patients who underwent partial or radical nephrectomy for pT2 or pT3 tumours at our centre between 1996 and 2011. Patients with benign masses, nodal positivity, or metastasis at the time of surgery were excluded. Using a competing-risks regression model, we compared differences in probability of recurrence between patients who used metformin versus those who did not. RESULTS: The patients on metformin at the time of surgery had worse disease recurrence than patients not on metformin. However, this was not statistically significant on multivariate analysis when controlling for age, race, body mass index, glomerular filtration rate, and tumour stage and grade (hazard ratio [HR], 1.22; 95% confidence interval [CI], 0.66-2.27 [p = 0.5]). Metformin use was associated with a lower risk of cancer-specific mortality, but this was not statistically significant when adjusted for clinical and tumour characteristics (HR, 0.76; 95% CI 0.21-2.7 [p = 0.7]). Limitations include the retrospective nature of the study and the lack on information on duration of metformin use. CONCLUSIONS: Metformin use at the time of surgery for high-risk clinically localized RCC is not protective in terms of recurrence or CSS. Further studies should be done to confirm these findings and determine what effect concurrent metformin use might have on improved response to targeted therapies in the metastatic setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".