Ready to eat cereal consumption with total and cause‐specific mortality: prospective analysis of 367,442 individuals (810.20)
Bibliographic record
Abstract
Background □ Intakes of ready to eat cereals (RTEC) have been inversely associated with the risk factors of chronic diseases such as cardiovascular disease (CVD), diabetes, and certain cancers; however their relations with total and cause‐specific mortality remain unclear. We prospectively assessed the associations of RTEC intakes with all causes and disease‐specific mortality risk. Methods and Results □ The study included 367,442 participants from the prospective NIH‐AARP Diet and Health Study. Intakes of RTEC were assessed at baseline. Over an average of 14 years of follow‐up, in total 46,067 deaths were documented. Consumption of RTEC was inversely associated with risk of mortality from all‐cause mortality and death from cancer, digestive cancer, CVD, and respiratory disease. In multivariable models, as compared with non‐consumers of RTEC, those in the highest intake of RTEC had a 15% lower risk of all‐cause mortality and 10‐30% lower risk of disease‐specific mortality such as deaths from CVD, diabetes, all cancers, and digestive caner (all P for trend < 0.05). Within RTEC consumers, total fiber intakes were associated with reduced risk of mortality from all‐cause mortality (15%) and deaths from CVD (12%), cancer (10%), digestive cancer (17%), and respiratory disease (10%). Conclusions □ Intakes of RTEC were inversely associated all‐cause mortality and disease‐specific mortality; Within RTEC consumers, higher intake of total fiber was associated with reduced risk of total and cause‐specific mortality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".