Acute post stroke depression at a Primary Stroke Center in the Middle East
Bibliographic record
Abstract
OBJECTIVE: Depression occurs in approximately 30 percent of stroke patients, leading to increased disability, lower quality of life and increased mortality. Given new recommendations to assess depression in acute stroke patients this study evaluated rates of acute post stroke depression at a Primary Stroke Center in Doha, Qatar. METHODS: Acute stroke patients (n = 233) were given the PHQ-9 and the Mini-Cog test by stroke unit nurses within the first few days post stroke. This was part of a clinical improvement project conducted from March 2016 thru March 2017. RESULTS: Approximately 20% of acute post stroke patients (46/233) scored in the moderately depressed range on the Patient Health Questionnaire (PHQ-9 ≥10 with item 1 and/or 2 endorsed). Nationality and dysarthria were significantly associated with depression. Females were twice as likely to be depressed. A significantly greater number of Middle Eastern and African patients were depressed (30.18%) than Southeast Asian and Western Pacific patients (16.76%). A PHQ-2 cut off of 2 was optimal with sensitivity of 91.3 and specificity of 71.6. CONCLUSIONS: Almost 20% of acute stroke patients were moderately depressed on the PHQ-9, with Middle Eastern/African patients almost twice as likely to be depressed. This may reflect higher baseline pre-stroke depression levels in those of Middle Eastern/African background, perhaps due to greater levels or stress or trauma exposure in these groups. Dysarthria was found to be significantly associated with depression. Initial screening with the PHQ-2 using a cut-off of 2 (versus the cut-off of 3 used in primary care settings) may be beneficial. Based on these results acute post stroke depression screening is recommended in the Middle East, coupled with culturally sensitive psychiatric care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".