Prevalence of poststroke anxiety and its associations with global cognitive impairment: An individual participant data analysis
Bibliographic record
Abstract
Anxiety is frequent after stroke; however, little is known about its determinants. This study aims to assess the prevalence and correlates of post stroke anxiety (PSA) within 3–6 months following ischemic stroke. Three cohort studies from the STROKOG consortium were involved. Demographic and clinical data were standardized. PSA and PSD were assessed using inventories. The criteria for post-stroke cognitive impairment (PSCI) were at least one cognitive domain impaired if applicable, or a Montreal Cognitive Assessment (MoCA) score. Descriptive analyses were conducted to ascertain the prevalence of anxiety. Comparisons between anxious and non-anxious patients in the total sample were made using χ 2 and t -tests. A two-step individual participant data (IPD) meta-analysis was employed to identify factors associated with PSA. 584 patients were included. The total prevalence of PSA was 35 % (95%CI = [31.23;38.97]) and ranged from 27 % to 45 % across cohorts. In the total sample, there was a higher proportion of females in the anxiety group than the non-anxiety group (χ 2 = 19.62; p < 0.001). Anxious patients had lower education, (χ 2 = 6.59; p = 0.03), higher stroke severity ( t = 2.77; p = 0.002), and higher rates of PSD (χ 2 = 118.09; p < 0.001), and PSCI (χ 2 = 23.81, p < 0.001). The analysis demonstrates that the odds of presenting with PSA is larger in patients with PSCI (OR = 1.84, 95%CI = [1.14; 2.91]). Anxiety is frequent after stroke, especially in females, and is associated with depression and cognitive impairment. • Three cohorts from the STROKOG consortium were included in the study. • PSA prevalence ranged from 25 to 45 % across cohorts. • In total, PSA was observed in 44.5 % of females and 28.5 % of males. • An IPD meta-analysis revealed that cognitive impairment is associated with PSA.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.016 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".