Diet therapy in patients with rare diseases: a scoping review
Bibliographic record
Abstract
BACKGROUND: This scoping review presents existing research evidence regarding diet therapy in patients with rare diseases (RDs). METHODS: Using the five-stage scoping review framework proposed by Arksey, O'Malley and Levac, we searched the published literature in PubMed, Web of Science, Royal Society of Chemistry, China National Knowledge Infrastructure, VIP Database and Wan Fang Database from January 2010 to November 2022. We selected diet therapy studies on 121 RDs, as categorised by the National Health Commission of China in 2018. Charts for research analysis were developed and used to categorise the data. RESULTS: We ultimately included 34 diet therapy studies from 19 countries and territories for 10 RDs and 3 RD groups. RD diet therapy studies have mainly focused on inborn errors of metabolism (92.3%) and are common in Western countries. Most studies focused on diet therapy methods for RDs (44%). In addition, 29% of studies included diet therapy management, 15% included guidelines for diet therapy and 12% included the impact of diet therapy on patients. CONCLUSIONS: Current diet therapies for RDs lack specificity and present with limited characteristics. Therefore, it is necessary to expand the scope and depth of future research and explore evidence-based recommendations and new diet therapies focused on patient needs and family support to provide a reference for improving the efficacy and safety of diet therapies for RDs.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".