Predictors for self-reported feeling of depression three months after stroke: A longitudinal cohort study
Bibliographic record
Abstract
OBJECTIVE: Depression and impaired cognition are common consequences of stroke. The aim of this study was to determine whether cognitive impairment 36-48 h post-stroke could predict self-reported feeling of depression 3 months post-stroke. DESIGN: A longitudinal, cohort study. PATIENTS: Patients aged ≥ 18 years at stroke onset. METHODS: Cognition was screened using the Montreal Cognitive Assessment, 36-48 h after admission to the stroke unit at Sahlgrenska University Hospital. Information about self-reported feeling of depression 3 months post-stroke was retrieved from Riksstroke (the national quality register for stroke in Sweden). Bootstrapped binary logistic regression analyses were performed. RESULTS: Of 305 patients, 42% were female, median age was 70 years, and 65% had mild stroke. Three months post-stroke, 56% of patients had self-reported feeling of depression; of these, 65% were female. Impaired cognition at baseline could not predict self-reported feeling of depression 3 months later. The odds for self-reported feeling of depression were twice as high in female patients (odds ratio 2.01; 95% confidence interval, 0.20-1.22; p < 0.01). CONCLUSION: Impaired cognition early after stroke could not predict self-reported feeling of depression 3 months post-stroke. Compared with male patients, female patients had twice the odds of self-reported feeling of depression.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".