A systematic review of the incidence and prevalence of autoimmune disease in multiple sclerosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it