A Prospective Study of Meat and Meat Mutagens and Prostate Cancer Risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High-temperature cooked meat contains heterocyclic amines, including 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), and polycyclic aromatic hydrocarbons, such as benzo(a)pyrene (BaP). In rodents, a high intake of PhIP induces prostate tumors. We prospectively investigated the association between meat and meat mutagens, specifically PhIP, and prostate cancer risk in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Diet was assessed using a 137-item food frequency questionnaire and a detailed meat-cooking questionnaire linked to a database for BaP and the heterocyclic amines 2-amino-3,8-dimethylimidazo[4,5-b]quinoxaline (MeIQx), 2-amino-3,4,8-trimethylimidazo[4,5-f]quinoxaline (DiMeIQx), and PhIP. During follow-up, we ascertained a total of 1,338 prostate cancer cases among 29,361 men; of these, 868 were incident cases (diagnosed after the first year of follow-up) and 520 were advanced cases (stage III or IV or a Gleason score of > or =7). Total, red, or white meat intake was not associated with prostate cancer risk. More than 10 g/d of very well done meat, compared with no consumption, was associated with a 1.4-fold increased risk of prostate cancer [95% confidence interval (95% CI), 1.05-1.92] and a 1.7-fold increased risk (95% CI, 1.19-2.40) of incident disease. Although there was no association with MeIQx and DiMeIQx, the highest quintile of PhIP was associated with a 1.2-fold increased risk of prostate cancer (95% CI, 1.01-1.48) and a 1.3-fold increased risk of incident disease (95% CI, 1.01-1.61). In conclusion, very well done meat was positively associated with prostate cancer risk. In addition, this study lends epidemiologic support to the animal studies, which have implicated PhIP as a prostate carcinogen.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it