The Effect of Vitamin D Supplements on Clinical and Para-Clinical Outcomes in Patients With Multiple Sclerosis: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is an inflammatory disease, which has a wide range of effects on patients. There are controversies regarding the role of vitamin D in clinical and laboratory improvements in MS patients. OBJECTIVE: The aim of this systematic review protocol is to evaluate the efficacy of vitamin D supplements on relapse rate, gadolinium-enhancing lesions of magnetic resonance imaging (MRI), and cytokine profiles. METHODS: We will search PubMed, Scopus, EMBASE, CINAHL, Web of Science, Ovid, ProQuest, American College of Physicians Journal Club database, Health Technology Assessment Database (The Cochrane Collaboration), and National Health System Economic Evaluation Database (The Cochrane Collaboration) and gray literature including reference of included studies and conference abstracts. Clinical trials reporting the effect of any doses of vitamin D on relapse rate, gadolinium-enhancing lesions of MRI, and cytokine profiles will be included. In total, 2 independent researchers will independently assess the studies, extract data, and evaluate the quality of primary studies. RESULTS: This systematic review was started in September 2017 and the process is continuing. The included articles are evaluated and researchers are going to extract the data. CONCLUSIONS: To our knowledge, this will be the first comprehensive systematic review aiming to assess the effect of vitamin D supplements on clinical and para-clinical outcomes in patients with multiple sclerosis. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12045.
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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 | 0.050 | 0.069 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.020 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.005 |
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".