Estimating the Effect of Adhering to the Recommendations of the 2019 Canada’s Food Guide on Health Outcomes in Older Adults: Protocol for a Target Trial Emulation (Preprint)
Bibliographic record
Abstract
BACKGROUND The 2019 Canada’s Food Guide provides universal recommendations to individuals aged ≥2 years. However, the extent to which these recommendations are appropriate for older adults is unknown. Although ideal, conducting a large randomized controlled trial is unrealistic in the short term. An alternative is the target trial emulation framework for causal inference, a novel approach to improve the analysis of observational data. OBJECTIVE This study aims to describe the protocol for a target trial emulation in older adults, with an emphasis on key aspects of a hypothetical sustained diet and physical activity intervention. METHODS To emulate the target trial, nonexperimental data from the Quebec Longitudinal Study on Nutrition and Successful Aging (NuAge; N=1753 adults aged ≥67 years) will be used. NuAge includes 4 yearly measurements of dietary intakes, covariates, and outcomes. The per-protocol causal contrast will be the primary causal contrast of interest to account for nonadherence. The sustained intervention strategy will be modeled using the parametric g-formula. In the hypothetical trial, participants will be instructed to meet sex-specific minimal intakes for vegetables and fruits, whole grains, animal- and plant-based protein foods, milk and plant-based beverages, and unsaturated fats. The eligibility criteria, follow-up, intervention, outcomes, and causal contrast in the emulation will closely align with those of the target trial, with only minor modifications. We will attempt to emulate the randomization of treatment by adjusting for baseline covariates and prebaseline dietary habits. RESULTS Data collection for NuAge was completed in June 2008. For this study, the main analysis was started in May 2024. Submission of the manuscript is expected by February 2025. CONCLUSIONS Emulating a target trial will provide the first evidence of the adequacy of the 2019 Canada’s Food Guide recommendations for older adults in relation to health outcomes. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/65182
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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 | 0.090 | 0.155 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.071 | 0.014 |
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".