A systematic review of the incidence and prevalence of autoimmune disease in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: As new therapies emerge which increase the risk of autoimmune disease it is increasingly important to understand the incidence of autoimmune disease in multiple sclerosis (MS). OBJECTIVE: The purpose of this review is to estimate the incidence and prevalence of comorbid autoimmune disease in MS. METHODS: The PUBMED, EMBASE, SCOPUS and Web of Knowledge databases, conference proceedings, and reference lists of retrieved articles were searched, and abstracts were independently screened by two reviewers. The data were abstracted by one reviewer using a standardized data collection form, and the findings were verified by a second reviewer. We assessed quality of the included studies using a standardized approach and conducted meta-analyses of population-based studies. RESULTS: Sixty-one articles met the inclusion criteria. We observed substantial heterogeneity with respect to the populations studied, methods of ascertaining comorbidity, and reporting of findings. Based solely on population-based studies, the most prevalent autoimmune comorbidities were psoriasis (7.74%) and thyroid disease (6.44%). Our findings also suggest an increased risk of inflammatory bowel disease, likely uveitis and possibly pemphigoid. CONCLUSION: Fewer than half of the studies identified were of high quality. Population-based studies that report age, sex and ethnicity-specific estimates of incidence and prevalence are needed in jurisdictions worldwide.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".