Commonly used diabetes and cardiovascular medications and cancer recurrence and cancer-specific mortality: a review of the literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it