Impact of diabetes and metformin use on prostate cancer outcome of patients treated with radiation therapy: results from a large institutional database.
Bibliographic record
Abstract
INTRODUCTION: Conflicting data exists on the influence of metformin on prostate cancer. We investigated the importance of metformin in patients treated with radiotherapy or brachytherapy. MATERIALS AND METHODS: All patients from a large institutionalized database, treated for primary localized prostate cancer with either brachytherapy or external-beam radiotherapy ± androgen deprivation therapy were identified. Groups were compared by Kaplan-Meier analyses and Cox regression models. Multivariate analysis was adjusted for CAPRA-Score, type of treatment and age. RESULTS: A total of 2441 patients with complete data was identified. Among the 382 patients (16% of total) were diabetic. Two-hundred and eighty-one of the 382 diabetics (74%) were treated with metformin and 101 were treated with other anti-diabetic medication. Median follow up was 48 months (interquartile range [IQR] 24-84). Two-hundred eighteen patients (9%) died and 150 (6%) experienced biochemical recurrence (BCR). On unadjusted univariate analysis for BCR-free survival, metformin users showed a 50% reduction in BCR compared to non-metformin users. The results remained significant on multivariate analysis comparing diabetic metformin users to non-metformin users (diabetics and non-diabetics combined) (hazard ratio [HR] 0.5-0.6, p = 0.03-0.04) but lost its significance when adjusting for cancer aggressiveness. On multivariate analysis, diabetics had worse overall survival (OS) than non-diabetics (HR 1.5, 95% confidence interval [CI] 1.08-2.06, p = 0.01), but diabetics on metformin fared better than diabetics not taking metformin (HR 0.5, 95% CI 0.26-0.86, p = 0.01). CONCLUSION: Metformin use in this analysis appears to be associated with better BCR and OS. Larger datasets and prospective trials are warranted to validate these results.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".