Comparison of Two Versions of the Hospital Anxiety and Depression Scale in Assessing Depression in a Neurologic Setting
Bibliographic record
Abstract
BACKGROUND: The Hospital Anxiety and Depression Scale-Depression Subscale (HADS-D) is widely used to assess depression in people with multiple sclerosis (MS). Developed specifically for use in a medical setting, the scale has one item, "I feel as if I am slowed down," that might represent a significant somatic confounder, possibly biasing the assessment. OBJECTIVE: We sought to determine whether inclusion or exclusion of the "slowed down" item in the HADS-D affects the detection of depression and the scale's associations with impaired cognition, fatigue, and employment status. METHODS: A sample of 193 people with confirmed MS completed the HADS. To identify depressed participants, we used previously established cutoff scores for the HADS-D with (≥8) and without (≥6) the "slowed down" item. Linear and logistic regression models were used to determine predictors of cognition and employment status. RESULTS: The HADS-D with and without the "slowed down" item detected similar rates of depression: 30.6% and 31.6%, respectively. Both versions of the HADS-D predicted processing speed and executive functioning, but not memory. Neither version predicted employment status. CONCLUSIONS: The HADS-D is an easy-to-use and clinically relevant self-report psychometric scale for detecting depression in MS. Removing the "slowed down" item from the HADS-D does not influence its internal consistency, and both versions have similar associations with clinical outcomes.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".