The validity and reliability of screening measures for depression and anxiety disorders in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: We aimed to evaluate the validity and reliability of multiple screening measures for depression and anxiety for use in the clinical care of people with multiple sclerosis (MS). METHODS: Participants with MS completed the Patient Health Questionnaire (PHQ-9), Hospital Anxiety and Depression Scale (HADS), Kessler-6 Distress Scale, PROMIS Emotional Distress Depression Short-Form 8a (PROMIS Depression) and Anxiety Short-Form 8a (PROMIS Anxiety), Generalized Anxiety Disorder 7-item Scale (GAD-7), and the Overall Anxiety and Severity Impairment Scale (OASIS). A subgroup repeated the screening measures two weeks later. All participants also completed a Structured Clinical Interview for DSM-IV-TR Axis I Disorders (SCID). For the screening measures we computed sensitivity, specificity, positive predictive and negative predictive value with SCID diagnoses as the reference standard and conducted receiver operating curve (ROC) analyses; we also assessed internal consistency and test-retest reliability. RESULTS: Of 253 participants, the SCID classified 10.3% with major depression and 14.6% with generalized anxiety disorder. Among the depression measures, the PHQ-9 had the highest sensitivity (84%). Specificity was generally higher than sensitivity, and was highest for the HADS-D with a cut-point of 11 (95%). In ROC analyses the area under the curve (AUC) did not differ between depression measures. Among the anxiety measures, sensitivity was highest for the HADS-A with a cut-point of 8 (82%). Specificity ranged from 83% to 86% for all measures except the HADS-A with a cut-point of 8 (68%). The AUC did not differ between anxiety measures. CONCLUSION: Overall, performance of the depression and anxiety screening measures was very similar, with reasonable psychometric properties for the MS population, suggesting that other factors such as accessibility and ease of use could guide the choice of measure in clinical practice.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".