Diabetes, Insulin Use, and Cancer Risk: Are Observational Studies Part of the Solution–or Part of the Problem?
Bibliographic record
Abstract
Cancer has overtaken cardiovascular disease asthe leading cause of death in individuals underthe age of 65 in the general population, but it isstill overshadowed by cardiovascular disease in those with diabetes. People with type 2 diabetes are nonetheless more likely to develop cancer—and to die from it—than members of the general population, so cancer should be numbered among the complications of diabetes (1). Furthermore, the number of cancer victims with diabetes will inevitably rise in proportion to our success in combating vascular disease in the diabetic population. How can the increased cancer risk in diabetes be explained? To begin with, it should be noted that obesity, insulin resistance, and/or increased levels of IGF-1 and insulin are strongly associated with most (but not all) of the diabetes-related cancers in the nondiabetic population (1). This suggests that hyperglycemia does not play an
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".