Depression in acute stroke
Bibliographic record
Abstract
Objective: Depression is one of the most frequent neuropsychiatric disturbances in stroke patients. The clinical aspects and correlations of depression in the first days after acute stroke are less known. This study aimed to 1) assess the frequency of depression, 2) describe the profile of depression of stroke patients and 3) analyze the relation between depression and demographic, predisposing and precipitating conditions, and clinical and imaging data, in acute stroke patients. Methods: We used the Montgomery–Asberg Depression Rating Scale to assess depression in 178 consecutive acute (≤ 4 days) stroke (26 subarachnoid hemorrhage, 31 intracerebral hemorrhage, 121 cerebral infarct) patients (mean age 57 yr) and in a control group of 50 acute coronary patients (mean age 59 yr). Results: Eightytwo patients (46%) presented acute depression; apathy/loss of interest was the most frequent clinical feature. In logistic regression, the best model to predict depression (backward model) identified previous mood disorder (odds ratio 2.2–12.9) as an independent predictor. There were no significant differences in the frequency or severity ( p > 0.45) of depression between control subjects and acute stroke patients. Conclusions: Depression was present in almost one-half of the acute stroke patients and was related to previous mood disorder but not not to stroke type or location. Apathy/loss of interest was the predominant clinical feature.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".