Vitamin D as a therapy for colitis: A systematic review
Bibliographic record
Abstract
BACKGROUND AND AIM: The effect of vitamin D supplementation on immune disorders has been a topical research focus. The aim of this systematic review was to examine the current evidence of the effect of vitamin D supplementation as a therapy for colitis. METHODS: The following databases were searched: MEDLINE, Pubmed, Scopus, Web of Knowledge, Cinicaltrials.gov and the Cochrane Central Register of Controlled Trials using the terms 'inflammatory bowel disease' 'Crohn's disease' 'ulcerative colitis' 'colitis' [and] 'vitamin D'. Both human and animal studies published in English language were examined. The reference lists of included studies and review articles were manually searched for any relevant studies. RESULTS: Four studies were included in this systematic review. All reported an improvement in disease activity with vitamin D supplementation. The only high quality human study reported a non-significant reduction of relapse rate for Crohn's disease. No major adverse effects of vitamin D supplementation were reported. CONCLUSIONS: Although there is some evidence that supplemental vitamin D, as an adjunctive treatment, may help in controlling colitis, this evidence is not enough to justify using vitamin D in treating inflammatory bowel disease (IBD). Large high quality placebo-controlled randomised controlled trials are needed to explore a possible benefit of using vitamin D in treating IBD.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".