Diet and Multiple Sclerosis: Scoping Review of Web-Based Recommendations
Bibliographic record
Abstract
BACKGROUND: There is currently no scientific evidence supporting the use of specific diets in the management of multiple sclerosis (MS); the strongest dietary associations are observed with vitamin D and omega-3 fatty acid supplementation. Despite this, there are many websites that provide advice or suggestions about using various dietary approaches to control symptoms or disease progression. OBJECTIVE: The objective of this study was to assess the dietary advice for the symptomatic management of MS available on the internet. METHODS: This study was a systematic review of webpages that provided dietary advice for the management of MS. Webpages were selected from an internet search conducted in November 2016 using Google, Yahoo, and Bing search engines and the search term "MS diet." The first two pages of results from each search engine were included for the initial assessment. Duplicates were removed. Data extracted from websites included specific advice relating to diet and its rationale and the citation of supporting scientific literature. Authorship and credential information were reviewed to assess webpage quality. RESULTS: We included 32 webpages in the final assessment. The webpages made a wide variety of specific recommendations regarding dietary patterns and individual foods to help manage MS. The most common dietary pattern advised on these webpages was the low-fat, high-fiber balanced diet, followed by the low-saturated fat diet, near-vegetarian Swank diet, and the Paleo diet. The main categories of individual foods or nutrients suggested for addition to the diet were: supplements (especially omega-3 and vitamin D), fruits, vegetables, and lean protein. In contrast, the most commonly recommended for removal were saturated fats, dairy, gluten-containing grains, and refined sugar. These recommendations were often accompanied by rationale relating to how the particular food or nutrient may affect the development, prevalence and symptoms of MS; however, very little of this information is supported by the current scientific evidence between diet and MS. Only 9 webpages provided full authorship including credential information. CONCLUSIONS: There is a wide variety of Web-based dietary advice, which in some cases is contradictory. In most cases, this advice is the result of peoples' individual experiences and has not been scientifically tested. How people living with MS use this information is not known. These findings highlight the important role health professionals can play in assisting people living with MS in their health information-seeking behaviors.
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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 | 0.019 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.032 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".