Simulation model to assess the validity of the clinical portfolio diet score used in the PortfolioDiet.app for dietary self-tracking: a secondary analysis of a randomized controlled trial in hyperlipidemic adults
Bibliographic record
Abstract
Introduction The Portfolio Diet combines cholesterol-lowering plant foods for the management of cardiovascular disease risk. However, the translation of this dietary approach into clinical practice necessitates a user-friendly method for patients to autonomously monitor their adherence. Objective This study aimed to develop and validate the clinical-Portfolio Diet Score (c-PDS) as a food-based metric to facilitate self-tracking of the Portfolio Diet. Methods Using a simulation model to estimate the c-PDS, the validity was assessed in a secondary analysis of a completed trial of the Portfolio Diet in 98 participants with hyperlipidemia over 6 months. Concurrent and predictive validity of the estimated c-PDS were assessed against the reference measure (weighed 7-day diet records) and concomitant changes in LDL-C from baseline to 6 months. Bland–Altman analysis was used to assess the limits of agreement between the two methods. Results The c-PDS was positively correlated with dietary adherence as measured using the 7-day diet records ( r = 0.94, p < 0.001). The c-PDS was negatively correlated with change in LDL-C ( r = −0.43, p < 0.001) with a 1-point increase in the c-PDS being associated with a − 0.04 mmol/L (CI:−0.06,−0.03; p < 0.001) or a 1.09% reduction in LDL-C. Visual evaluation of the Bland–Altman plots showed reasonable agreement. Conclusion These findings indicate good validity of the c-PDS for primary prevention in adults with hyperlipidemia. The predictive validity findings have informed the goals and messaging within the PortfolioDiet.app, a digital health application for delivering the Portfolio Diet. Future research will assess the effectiveness of the intended combination of the c-PDS and the PortfolioDiet.app in supporting behavior change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".