Investigating Metformin for Cancer Prevention and Treatment: The End of the Beginning
Bibliographic record
Abstract
UNLABELLED: Laboratory research and pharmacoepidemiology are providing converging evidence that the widely used antidiabetic drug metformin has antineoplastic activity, but there are caveats. Although population studies suggest that metformin exposure is associated with reduced cancer risk and/or improved prognosis, these data are mostly retrospective and nonrandomized. Laboratory models show antineoplastic activity, but metformin concentrations used in many experiments exceed those achieved with conventional doses used for diabetes treatment. Ongoing translational research should be useful in guiding design of clinical trials, not only to evaluate metformin at conventional antidiabetic doses, where reduction of elevated insulin levels may contribute to antineoplastic activity for certain subsets of patients, but also to explore more aggressive dosing of biguanides, which may lead to reprogramming of energy metabolism in a manner that could provide important opportunities for synthetic lethality through rational drug combinations or in the context of genetic lesions associated with hypersensitivity to energetic stress. SIGNIFICANCE: There are tantalizing clues that justify the investigation of antineoplastic activities of biguanides. The complexity of their biologic effects requires further translational research to guide clinical trial design.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| 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".