Metformin and Cancer: Mounting Evidence Against an Association
Bibliographic record
Abstract
Metformin, a biguanide derived from the French lilac, has become the preferred first-line therapy for the treatment of type 2 diabetes (1). This drug is inexpensive, has an excellent safety profile, and can be safely combined with other antidiabetes agents (2). As a result, it has become the most widely prescribed antidiabetes drug worldwide. In addition to metformin’s well-established antidiabetes effects, there has been considerable interest in its antitumor properties. Such interest started from a short report of an observational study published in 2005 that suggested that the use of metformin was associated with a 23% decreased risk of any cancer (3). Since then, a large number of observational studies have been published with several “corroborating” a possible decreased incidence of cancer with this drug (4). In parallel, several laboratory studies have also suggested that metformin has antineoplastic activity, although doses used in such experiments were typically higher than the conventional doses used in the treatment of type 2 diabetes (5). Nonetheless, this apparent convergence of evidence from both observational and laboratory studies has led some to call for large randomized clinical trials (RCTs) of metformin in cancer prevention and treatment (6–9). However, a careful assessment of the observational studies conducted to date point to some important time-related biases that systematically exaggerated the reported antitumor effects of metformin (10). Time-related biases, such as immortal time bias, time window bias, and time lag bias, have been previously described in studies of diabetes treatment (10) and in other therapeutic areas (11–14). These biases result from not properly classifying exposure during the follow-up of a cohort study or from …
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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".