Perceptions and Utilization of Registered Dietitian Nutritionists in Multiple Sclerosis Care: A Pilot Survey of Multidisciplinary Providers
Bibliographic record
Abstract
Background/Objectives: Registered dietitian nutritionists (RDNs) are allied healthcare professionals who can help people with multiple sclerosis (MS) incorporate healthy eating, but little is known about their involvement in MS care. Thus, the objective of this survey was to investigate the perceptions and utilization of RDNs in MS care among multidisciplinary MS providers in the United States and Canada. Methods: An online survey was disseminated via the Consortium of MS Centers email listserv and MS-specific scientific conferences. The survey queried practitioner type, RDN referrals, the perceived benefits of RDNs in MS care, and the proportion of their patients who follow ‘MS diets’ or have nutrition-related issues. Reasons for or against RDN referral and beneficial resources were also queried. Results: Of the 60 completed surveys, respondents were primarily neurologists (n = 27, 45.0%). Most (n = 43, 71.7%) indicated that half or more of their patients inquire about diet, but n = 32 (53.3%) indicated that very few follow an ‘MS diet’ and n = 47 (78.3%) indicated that very few decline disease-modifying therapies to follow an ‘MS diet’. Most (n = 45, 77.6%) respondents indicated referring their patients to a RDN with lack of nutrition knowledge/general healthy eating advice (n = 34, 73.9%) and overweight/obesity (n = 31, 67.4%) as being the most common reasons for referral. RDNs were reported as being helpful or extremely helpful by n = 38 (84.4%) of respondents who reported referring to RDNs. Most (n = 46, 79.3%) indicated that their patients would benefit from having an RDN with MS-specialized training as a member of staff. Conclusions: MS care providers support the need for RDNs with specialized training in MS care.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".