Impact of Stress, Anxiety, and Depression on Cognition and Quality of Life in Stroke Patients
Bibliographic record
Abstract
Context: The pandemic has resulted in rapid spread of disease-causing fear in adults, children and unhealthy people. The enactment of national policies resulted in reduced outpatient visits in hospitals and perceived stress, anxiety and depression have become very common in neurological ill patients impacting their cognition and affecting their quality of life (QOL). Cognitive behavioral therapy (CBT) is a sort of neuropsychiatric treatment which helps patients to cope up with stress, anxiety, and depression and it needs further research. Aim: The present study focuses on the understanding the effect of anxiety, stress, and depression on the cognition and QOL of neurological ill patents and the influence of CBT in improving the cognitive ability of stroke patients. Settings and Design: This experimental study was conducted on neurologically ill patients Materials and Methods Thirteen subjects diagnosed with stroke were given 3-week protocol for CBT. Pre- and postassessment was done to evaluate depression, stress, anxiety, cognition and QOL on the basis of outcome measures including Depression Anxiety Stress Scale-21 and Montreal Cognitive Assessment Test for cognition and Neuro-QOL Brief for QOL. Statistical Analysis Used: Correlation between DASS-21 and MoCA was done using Pearson's correlation coefficient test. Results: 40% of the patients suffered from depression, stress, and anxiety. The results of paired t -test for pre- and poststress, depression, and anxiety showed a significant result. There was a significant difference in the scores of precognition and postcognition. Conclusion: CBT is effective in reducing perceived stress, depression, and anxiety and thereby improving cognition and QOL of stroke patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".