Validating screening tools for depression in stroke and transient ischemic attack patients
Bibliographic record
Abstract
OBJECTIVE: The best screening questionnaires for detecting post-stroke depression have not been identified. We aimed to validate four commonly used depression screening tools in stroke and transient ischemic attack patients. METHODS: Consecutive stroke and transient ischemic attack patients visiting an outpatient stroke clinic in Calgary, Alberta (Canada) completed a demographic questionnaire and four depression screening tools: Patient Health Questionnaire (PHQ)-9, PHQ-2, Hospital Anxiety and Depression Scale (HADS-D), and Geriatric Depression Scale (GDS-15). Participants then completed the Structured Clinical Interview for DSM-IV (SCID), the gold-standard for diagnosing major depression. The questionnaires were validated against the SCID and sensitivity and specificity were calculated at various cut-points. Optimal cut-points for each questionnaire were determined using receiver-operating curve analyses. RESULTS: Among 122 participants, 59.5% were diagnosed with stroke and 40.5% with transient ischemic attack. The point prevalence of SCID-diagnosed current major depression was 9.8%. At the optimal cut-points, the sensitivity and specificity for each screening tool were as follows: PHQ-9 (sensitivity: 81.8%, specificity: 97.1%), PHQ-2 (sensitivity: 75.0%, specificity: 96.3%), HADS-D (sensitivity: 63.6%, specificity: 98.1%), and GDS-15 (sensitivity: 45.5%, specificity: 84.8%). Areas under the receiver operating characteristic curves were as follows: PHQ-9 86.6%, PHQ-2 86.7%, HADS-D 85.9%, and GDS-15 66.3%. CONCLUSIONS: The PHQ-2 and PHQ-9 are both suitable depression screening tools, taking less than 5 minutes to complete. The HADS-D does not appear to have any advantage over the PHQ-based scales, even though it was designed specifically for medically ill populations. The GDS-15 cannot be recommended for general use in a stroke clinic based on this study as it had worse discrimination due to low sensitivity.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".