The Relationship between Anxiety and Cognition in Stroke Patients: A Cross-Sectional Study
Bibliographic record
Abstract
Background: There is limited evidence on the relationship between anxiety and cognition in stroke patients, and no precise relationship between the two has been indicated. Objective: We aimed to explore the precise relationship between anxiety and cognition in Chinese stroke patients. Methods: This study was a cross-sectional study, 384 hospitalized stroke patients were assessed with questionnaires and scales, including the Demographic Characteristics Questionnaire, the Hamilton Anxiety Inventory (HAMA), and the Montreal Cognitive Assessment (MoCA). Results: Anxiety was present in 55.47% of the 384 patients. Univariate analysis showed that age, gender, marital status, smoking, and alcohol consumption were associated with cognition, and multiple linear regression results showed that HAMA was not independently associated with MoCA after adjusting for potential confounders (β=-0.16, 95% CI: -0.29 to- 0.03), which would be inconsistent with HAMA (subgroup) as a categorical variable (P trend of 0.004) A non-linear relationship was detected between HAMA and MoCA with an inflection point of 9. The effect sizes and confidence intervals to the left and right of the inflection point were -0.54 (-0.78 to -0.30) and 0.02 (-0.14 to -0.17), respectively. Conclusion: The relationship between anxiety and cognition is nonlinear. When the HAMA score is less than 9, anxiety and cognition are negatively correlated, and when it is greater than or equal to 9, the cognitive score will no longer decrease and is saturated.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".