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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".