Commonly used diabetes and cardiovascular medications and cancer recurrence and cancer-specific mortality: a review of the literature
Bibliographic record
Abstract
INTRODUCTION: Cancer most commonly arises in the elderly who are often burdened with comorbidities. Medications used for treating these comorbidities may alter cancer prognosis. Understanding the impact of these medications on cancer is important in order to make effective evidence-based decisions about managing comorbidities while improving cancer outcomes. AREAS COVERED: The evidence on diabetes, statins, antihypertensive and anti-inflammatory medications and their association with cancer recurrence and cancer-specific mortality are reviewed. The strengths and limitations of the existing literature, the current state of the field and future directions are discussed. EXPERT OPINION: Metformin and aspirin were associated with a reduced risk of cancer recurrence and cancer-specific mortality. The evidence for statins and antihypertensive medications on cancer survival was inconsistent. There were few studies to suggest that any of the medication classes of interest were associated with negative effects on cancer survival. Methodological shortcomings within observational studies, such as confounding, distinguishing between use of medications pre-cancer versus post-cancer diagnosis/treatment, misclassification of exposures/outcomes, informative censoring and competing risks, must be considered. New observational studies addressing these limitations are essential. Some clinical trials are underway to further investigate the beneficial effects of these drugs and completed trials have confirmed results demonstrated in observational studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".