Bibliographic record
Abstract
The authors conducted a population-based, case-control study of 21,022 incident cases of 19 types of cancer and 5,039 controls aged 20-76 years during 1994-1997 to examine the association between obesity and the risks of various cancers. Compared with people with a body mass index of less than 25 kg/m(2), obese (body mass index of > or = 30 kg/m(2)) men and women had an increased risk of overall cancer (multivariable adjusted odds ratio = 1.34, 95% confidence interval (CI): 1.22, 1.48), non-Hodgkin's lymphoma (odds ratio = 1.46, 95% CI: 1.24, 1.72), leukemia (odds ratio = 1.61, 95% CI: 1.32, 1.96), multiple myeloma (odds ratio = 2.06, 95% CI: 1.46, 2.89), and cancers of the kidney (odds ratio = 2.74, 95% CI: 2.30, 3.25), colon (odds ratio = 1.93, 95% CI: 1.61, 2.31), rectum (odds ratio = 1.65, 95% CI: 1.36, 2.00), pancreas (odds ratio = 1.51, 95% CI: 1.19, 1.92), breast (in postmenopausal women) (odds ratio = 1.66, 95% CI: 1.33, 2.06), ovary (odds ratio = 1.95, 95% CI: 1.44, 2.64), and prostate (odds ratio = 1.27, 95% CI: 1.09, 1.47). Overall, excess body mass accounted for 7.7% of all cancers in Canada-9.7% in men and 5.9% in women. This study provides further evidence that obesity increases the risk of overall cancer, non-Hodgkin's lymphoma, leukemia, multiple myeloma, and cancers of the kidney, colon, rectum, breast (in postmenopausal women), pancreas, ovary, and prostate.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".