Hype versus Hope: Metformin and Vitamin D as Anticancer Agents
Bibliographic record
Abstract
There has been increasing interest in the use of metformin and vitamin D to reduce cancer risk and improve outcomes. Metformin, an oral antidiabetic drug, improves insulin resistance and has been associated with reduced cancer incidence and cancer mortality. Low levels of vitamin D have also been associated with increased cancer risk, mainly in retrospective studies, and it has been suggested that vitamin D supplementation might play a role in cancer prevention. Preclinical data provide a biologic rationale for these associations; however, the human data arise predominantly from observational studies and caution is needed in their translation into clinical practice. This is because of the recognized limitations of observational studies, such as time-related survival biases, selection and referral biases, short follow-up, and the presence of confounding factors that can lead to spurious or inaccurate findings. Combined examination of associations with cancer risk and outcome (occurring when exposure in a population that does not yet have cancer is analyzed in relation to cancer death) may yield results that are difficult to interpret. Finally, associations across all cancers may differ from those in specific cancer types. These shortcomings can be overcome in properly designed and adequately powered prospective randomized trials; however, such trials are both expensive and time consuming. We review the literature examining the associations of metformin and vitamin D with cancer, discussing weakness and strengths and making recommendations for further research and clinical practice.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".