The incidence and prevalence of psychiatric disorders in multiple sclerosis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Psychiatric comorbidity is associated with lower quality of life, more fatigue, and reduced adherence to disease-modifying therapy in multiple sclerosis (MS). OBJECTIVES: The objectives of this review are to estimate the incidence and prevalence of selected comorbid psychiatric disorders in MS and evaluate the quality of included studies. METHODS: We searched the PubMed, PsychInfo, SCOPUS, and Web of Knowledge databases and reference lists of retrieved articles. Abstracts were screened for relevance by two independent reviewers, followed by full-text review. Data were abstracted by one reviewer, and verified by a second reviewer. Study quality was evaluated using a standardized tool. For population-based studies we assessed heterogeneity quantitatively using the I² statistic, and conducted meta-analyses. RESULTS: We included 118 studies in this review. Among population-based studies, the prevalence of anxiety was 21.9% (95% CI: 8.76%-35.0%), while it was 14.8% for alcohol abuse, 5.83% for bipolar disorder, 23.7% (95% CI: 17.4%-30.0%) for depression, 2.5% for substance abuse, and 4.3% (95% CI: 0%-10.3%) for psychosis. CONCLUSION: This review confirms that psychiatric comorbidity, particularly depression and anxiety, is common in MS. However, the incidence of psychiatric comorbidity remains understudied. Future comparisons across studies would be enhanced by developing a consistent approach to measuring psychiatric comorbidity, and reporting of age-, sex-, and ethnicity-specific estimates.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.012 | 0.014 |
| 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) | 0.002 | 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".