Anxiety disorders and their clinical correlates in multiple sclerosis patients
Bibliographic record
Abstract
OBJECTIVE: To assess prevalence rates and clinical correlates of anxiety disorders in patients with multiple sclerosis (MS). METHODS: Demographic and neurological data were collected on 140 consecutive clinic attendees, and their lifetime and point prevalences of anxiety disorders were determined with the Structured Clinical Interview for DSM-IV disorders (SCID-IV). All subjects completed the self-report Hospital Anxiety and Depression Scale (HADS). Suicidal intent was rated with the Beck Suicide Scale (BSS), psychosocial stressors and supports were quantified with Social Stress and Support Interview (SSSI), and cognition assessed with Neuropsychological Screening Battery for MS. RESULTS: The lifetime prevalence of any anxiety disorder was 35.7%, with panic disorder (10%), obsessive compulsive disorder (8.6%), and generalized anxiety disorder (18.6%), the most common diagnoses obtained. Subjects with an anxiety disorder were more likely to be female, have a history of depression, drink to excess, report higher social stress and have contemplated suicide. The diagnosis of an anxiety disorder had been missed in the majority of subjects, therefore, they had not received treatment. A discriminant function analysis identified a series of variables that correctly classified 75% of patients with an anxiety disorder. CONCLUSION: Anxiety disorders are common in patients with MS, but are frequently overlooked and under-treated. Risk factors include being female, a co-morbid diagnosis of depression, and limited social support. Clinicians should evaluate all MS subjects for anxiety disorders, as they represent a treatable cause of disability in 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".