Validation of the Hospital Anxiety and Depression Scale for use with multiple sclerosis patients
Bibliographic record
Abstract
Detecting clinically significant symptoms of depression and anxiety in medically ill patients using self-report rating scales presents a challenge because of somatic confounders. The Hospital Anxiety and Depression Scale (HADS) was developed with this in mind, but has never been validated for a multiple sclerosis population. Our objective was to validate the HADS for multiple sclerosis patients. Multiple sclerosis patients were interviewed for the presence of major depression (n = 180) and anxiety disorders (n = 140) with the Structured Clinical Interview for DSM-IV disorders. A receiver operating characteristic (ROC) analysis was undertaken to assess which HADS cut-off scores give the best yield with respect to diagnoses of major depression and all anxiety disorders defined by the Structured Clinical Interview for DSM-IV. A threshold score of 8 or greater on the HADS depression subscale provides a sensitivity of 90% and specificity of 87.3% (ROC area under the curve 0.938). The same cut-off score gives a sensitivity of 88.5% and a specificity of 80.7% on the anxiety subscale (ROC area under the curve 0.913), but for generalized anxiety disorder only. The study confirms the usefulness of the HADS as a marker of major depression and generalized anxiety disorder, but not other anxiety disorders, in multiple sclerosis patients.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".