The incidence and prevalence of comorbid gastrointestinal, musculoskeletal, ocular, pulmonary, and renal disorders in multiple sclerosis: A systematic review
Bibliographic record
Abstract
BACKGROUND: As new disease-modifying therapies emerge a better knowledge of the risk of comorbid disease in multiple sclerosis (MS) is needed. OBJECTIVE: To estimate the incidence and prevalence of comorbid gastrointestinal, musculoskeletal, ocular, pulmonary, and renal disorders in MS. METHODS: We systematically reviewed the world literature by searching PUBMED, EMBASE, SCOPUS, the Web of Knowledge, and reference lists of retrieved articles. For selected articles, one reviewer abstracted data using a standardized form. The abstraction was verified by a second reviewer. The quality of all selected studies was assessed. For population-based studies we quantitatively assessed studies using the I² statistic, and conducted random effects meta-analyses. RESULTS: Study designs were heterogeneous with respect to populations, case definitions, and methods of ascertainment. Incidence of the studied comorbidities was rarely reported. Irritable bowel syndrome and chronic lung disease had a prevalence of more than 10% in the MS population. Irritable bowel syndrome, fibromyalgia, cataracts and glaucoma were more common than expected in the MS population. CONCLUSION: Although they have been the subject of less study than other comorbidities, irritable bowel syndrome, arthritis, and chronic lung disease are common in the MS population and occur more often than expected when compared to the general population.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".