Social anxiety in a multiple sclerosis clinic population
Bibliographic record
Abstract
BACKGROUND: Little is known about social anxiety in MS. OBJECTIVE: We estimated the prevalence of social anxiety symptoms and their association with demographic and clinical features in a clinic-attending sample of patients with MS. METHODS: Patients attending the Dalhousie MS Research Unit for regularly scheduled visits completed the Social Phobia Inventory (SPIN), the Hospital Anxiety and Depression Scale (HADS), and the Health Utilities Index (HUI). Neurological disability was determined by ratings on the Expanded Disability Status Scale (EDSS). RESULTS: A total of 251 patients completed self-report scales of anxiety and depression symptoms. In all, 245 (98%) provided sufficient data for analysis. In all, 30.6% (n=75) had clinically significant social anxiety symptoms as defined by a SPIN threshold score of 19. Half of those with social anxiety had general anxiety (HADSA>or=11) and a quarter had depression (HADSD>or=11). Severity of social anxiety symptoms was associated with reduced health-related quality of life and not related to neurological disability. CONCLUSIONS: Social anxiety symptoms are common in persons with MS, contribute to overall morbidity, but are unrelated to the overall severity of neurologic disability. Greater awareness and routine systematic inquiry of social anxiety symptoms is an important component of comprehensive care for persons with MS.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".