The burden of mental comorbidity in multiple sclerosis: frequent, underdiagnosed, and undertreated
Bibliographic record
Abstract
BACKGROUND: Mental comorbidity is common in multiple sclerosis (MS), but some studies suggest that mental comorbidity may be underrecognized and undertreated. OBJECTIVE: Using the North American Research Committee on MS Registry, we assessed the frequency of mental comorbidities in MS and sociodemographic characteristics associated with diagnosis and treatment of depression. METHODS: We queried participants regarding depression, anxiety, bipolar disorder, and schizophrenia. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CESD); a score>or=21 indicated probable major depression. RESULTS: Mental comorbidity affected 4264 (48%) responders; depression most frequently (4012, 46%). Among participants not reporting mental comorbidity, 751 (16.2%) had CESD scores>or=21 suggesting undiagnosed depression. Lower socioeconomic status was associated with increased odds of depression (Income $15,000-30,000 vs >$100,000 OR 1.34; 1.11-1.62), undiagnosed depression (Income $15,000-30,000 vs >$100,000 OR 1.52; 1.08-2.13), and untreated depression (
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".