A systematic review of oral health in people with multiple sclerosis
Bibliographic record
Abstract
OBJECTIVES: Despite more than 25 years of research focused on this topic, it remains unclear whether people with multiple sclerosis are more likely to present with oral health problems. The aim of this study was to provide the first systematic review of this literature. METHODS: A literature search for studies focused on oral health and multiple sclerosis was conducted using PRISMA guidelines. Electronic databases (PubMed, Scopus, Web of Science, MEDLINE and CINAHL) were searched up until February 2019. Two independent coders extracted data, and study quality graded using the Newcastle-Ottawa Scale (NOS). RESULTS: From 1281 articles identified, 17 met all the eligibility criteria. Of the seventeen studies, more than half included a nonclinical control group, and the majority were observational studies. The included studies were of poor to moderate quality. Taken together, the results provided only very limited evidence that people with multiple sclerosis are more likely to present with dental caries and gingival disease. There was suggestive evidence that people with multiple sclerosis may be at higher risk of periodontal disease and present with poorer oral hygiene, and moderate evidence for an association between multiple sclerosis and temporomandibular disorders. CONCLUSIONS: This systematic review provides evidence of an association between multiple sclerosis and at least some oral health problems. When temporomandibular disorders and periodontal status specifically have been assessed, most studies provide evidence of an association with multiple sclerosis. However, this review also clearly highlights the need for further, high-quality studies in this area.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".