Estimating the effect of adhering to Canada's Food Guide 2019 recommendations on health outcomes in older adults: a target trial emulation protocol
Bibliographic record
Abstract
Background: The Canada's Food Guide 2019 (CFG) provides universal recommendations to individuals aged 2 years or older. 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: Our aim is to describe the protocol of a target trial emulation in older adults with emphasis on key aspects of a hypothetical sustained diet and physical activity intervention. Methods: To emulate the target trial, non-experimental data from the NuAge prospective study (n=1753, adults aged 67 years or older) 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 non-adherence. The sustained intervention strategy will be modelled using the parametric g-formula. In the hypothetical trial, participants would be instructed to meet sex-specific minimal intakes for vegetables and fruits, whole grains, animal- and plant-based protein foods, milk & plant-based beverages and unsaturated fats. Eligibility criteria, follow-up, intervention, outcomes, and causal contrast will be similar in the emulation to the target trial except for minor modifications. We will attempt to emulate randomization of treatment by adjusting for baseline covariates and pre-baseline dietary habits. Conclusion: Emulating a target trial will provide the first evidence of the adequacy of CFG 2019 recommendations for older adults in relation to health outcomes.
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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 | 0.125 | 0.161 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".