Bibliographic record
Abstract
At a symposium discussing controversies pertaining to relationships between diabetes and cancer, Jeffrey A. Johnson (Edmonton, Canada) reviewed epidemiologic data, beginning with a meta-analysis showing that diabetes is associated with increased rates of cancers of the pancreas, colon and rectum, bladder, liver, and breast; endometrial cancer; and non-Hodgkin's lymphoma. Prostate cancer rates are decreased, perhaps as a consequence of a subtle form of hypogonadism, but prostate cancer mortality is increased among diabetic men who do develop prostate cancer. Obesity increases the development of cancers as well, to a greater degree with greater levels of obesity, particularly for cancers of the esophagus and thyroid and, among women, cancers of the endometrium, gallbladder, colon, and kidney. Cancer mortality increases by ∼50% in both sexes in association with obesity (1). The interesting exception to the generally adverse association of obesity with malignancy is its negative relationship with lung cancer, with cigarette use the presumed confounder by its weight-reducing effect (2). The mechanism of the relationship between diabetes and cancer has not been defined in clinical studies. Johnson's meta-analysis of trials of glycemic control did not show an effect on the risk of developing malignancy (3). Hyperglycemia was, however, associated with cancer mortality in 10-year studies of >1 million Korean (4) and >500,000 European (5) men and women, with the studies controlling for obesity though possibly reflecting a role of hyperinsulinemia. A role of hyperinsulinemia is further suggested by studies showing association of C-peptide with colorectal cancer risk (6,7). Reduced cancer survival seen in individuals with diabetes (8) may be, at least to an extent, due to diabetes-related diseases other than the malignancy itself (9) or to diabetic individuals having a lower likelihood of undergoing mammography, resulting in presentation with later-stage tumors (10). Lower rates of Pap test screening for cervical …
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.017 | 0.033 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".