The burden of mental comorbidity in multiple sclerosis: frequent, underdiagnosed, and undertreated
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: 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 (<high school vs postgraduate degree OR 3.13; 1.65-5.99). CONCLUSIONS: Mental comorbidity remains underdiagnosed and undertreated in MS. Patients of lower socioeconomic status bear a disproportionate share of the burden of depression.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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