Development and Evaluation of the Dietary Pattern Calculator (DiPaC) for Personalized Assessment and Feedback
Bibliographic record
Abstract
This study aimed to develop and validate a diet assessment screener – the Dietary Pattern Calculator (DiPaC). A scoping review identified currently available short diet quality assessment tools. Twenty-one articles covering 19 unique tools were included. The current tools mainly focused on individual nutrients or food groups or were developed for a specific population, and few ascertained overall dietary patterns. The 24-hour dietary recalls from the nationally representative Canadian Community Health Survey (CCHS)-Nutrition 2015 (n = 13,958) were used to derive and validate a personalized dietary pattern informed by the scoping review using weighted partial least squares. The dominant dietary pattern in CCHS-Nutrition 2015 was characterized by high consumption of fast foods, carbonated drinks, and salty snacks and low consumption of whole fruits, orange vegetables, other vegetables and juices, whole grains, dark green vegetables, legumes, and soy. The dietary pattern assessment was used to create and evaluate DiPaC following an agile and user-centred research and development approach. DiPaC, which demonstrated high validity and intermediate reliability (internal consistency = 0.47–0.51), is publicly available at https://www.projectbiglife.ca/ . DiPaC can be used by the public, clinicians, and researchers for quick and robust assessment of diet quality, providing immediate feedback with the advantage of being easy to implement.
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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 | 0.123 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".