Metabolic Diet App Suite for inborn errors of amino acid metabolism
Bibliographic record
Abstract
BACKGROUND: An increasing number of rare inborn errors of metabolism (IEMs) are amenable to targeted metabolic nutrition therapy. Daily adherence is important to attain metabolic control and prevent organ damage. This is challenging however, given the lack of information of disorder specific nutrient content of foods, the limited availability and cost of specialty products as well as difficulties in reliable calculation and tracking of dietary intake and targets. OBJECTIVES: To develop apps for all inborn errors of amino acid metabolism for which the mainstay of treatment is a medical diet, and obtain patient and family feedback throughout the process to incorporate this into subsequent versions. METHODS & RESULTS: The Metabolic Diet App Suite was created with input from health care professionals as a free, user-friendly, online tool for both mobile devices and desktop computers (http://www.metabolicdietapp.org) for 15 different IEMs. General information is provided for each IEM with links to useful online resources. Nutrient information is based on the MetabolicPro™, a North American food database compiled by the Genetic Metabolic Dietitians International (GMDI) Technology committee. After user registration, a personalized dashboard and management plan including specific nutrient goals are created. Each Diet App has a user-friendly interface and the functions include: nutrient intake counts, adding your own foods and homemade recipes and, managing a daily food diary. Patient and family feedback was overall positive and specific suggestions were used to further improve the App Suite. DISCUSSION: The Metabolic Diet App Suite aids individuals affected by IEMs to track and plan their meals. Future research should evaluate its impact on patient adherence, metabolic control, quality of life and health-related outcomes. The Suite will be updated and expanded to Apps for other categories of IEMs. Finally, this Suite is a support tool only, and does not replace medical/metabolic nutrition professional advice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".