Eating Timing and Frequency as a Predictor of Hospitalization and/or Mortality From Coronary Artery Disease: The Linked CCHS-DAD-CMDB 2004-2013 Study
Bibliographic record
Abstract
Background: Coronary artery disease (CAD) is a major overlapping challenge in both clinical and public health realms due to high rates of hospitalization and mortality. Despite nutrition being a key risk factor for CAD, little is known about eating timing and frequency in Canadians or their relation to risk of hospitalization and/or mortality from CAD. Methods: Breakfast consumption, between-meal consumption, eating frequency, and established CAD risk factors were assessed at baseline in 13,328 adults free of cancer and CAD from the 2004 Canadian Community Health Survey, Cycle 2.2, Nutrition Focus and were linked to administrative health databases to determine incidence of hospitalization and/or mortality from CAD in the following 9 years. Multivariable-adjusted hazard ratios with 95% confidence intervals estimated from Cox proportional hazards models were computed to test for associations between eating timing/frequency and hospitalization and/or mortality from CAD (n = 746 cases). Results: Skipping breakfast (12.0% of participants) and engaging in between-meal consumption (90.2%) were common practices, as was eating 4-5 times per day (55.2%). Skipping breakfast, between-meal consumption, and eating more or less than 4-5 times/day were strongly and bi-directionally associated with many sociodemographic, lifestyle, and metabolic risk factors at baseline. No associations were observed between skipping breakfast, between-meal consumption, or eating frequency and risk of hospitalization and/or mortality from CAD. Conclusions: Skipping breakfast, between-meal consumption, and eating frequency were associated with numerous established risk and preventative factors for CAD at baseline but were not directly associated with the risk of hospitalization and/or mortality from CAD in this cohort of Canadian adults.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".