Insulin analogues and cancer risk: cause for concern or<i>cause célèbre</i>?
Bibliographic record
Abstract
People with diabetes, particularly those with type 2 diabetes, may be at an increased risk of cancer. Furthermore, their cancer risk may be modified by treatment choices. In this respect, metformin may be protective, whereas insulin and insulin analogues can function as growth factors and therefore have theoretical potential to promote tumour proliferation. Analogues causing inappropriate prolonged stimulation of the insulin receptor, or excess stimulation of the IGF-1 receptor, are the most likely to show mitogenic properties in laboratory studies. Some recent epidemiological studies appear to be consistent with these experimental findings, suggesting that there could be different relative risks for cancer associated with different insulins, although these studies have attracted some methodological criticism. However, it is biologically plausible that hormonal factors that influence neoplasia could begin to manifest their effects in surprisingly short timescales (within 2 years) and hence these epidemiological studies justify further research. Even if future research were to document an increase in cancer risk among insulin users, this would be unlikely to significantly diminish the favourable benefit-risk ratio for patients requiring insulin therapy. There is a need for further population studies and for the development of new laboratory models that are more sophisticated than previous experimental methods employed to assess potential tumour growth-promoting properties of insulins.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".