Cognitive Impairment and Its Impact on Quality of Life in Rural Indian Female after Stroke: A Cross Sectional Study Protocol
Bibliographic record
Abstract
Introduction: Stroke represented growing social, health care and economic problems. in 2005 vascularcognitive impairment a condition that can be found in 20-30% of stroke patient. Now in 2018-19 over 50percent of stroke survivors have reported cognitive impairment 6 months after stroke and are associated withpoorer quality of life which increase disability. Cognitive functions is identified as a top priority for strokeresearch. Cognitive impairment affects inadequate ability to focus on the job, recall, understand, prepare, useknowledge, initiate and stop the operation and solve problem. a stroke impaired cognitive function includingfocus, memory, vocabulary, executive function, perception and orientation of space. Because of abnormalityin functional independence and other abnormalities in higher function, cognitive impairment may lead toaffect independence. it increases the death ratio, abnormality. Stroke impacts wellbeing dramatically onhealth system resulting in high costs, and is also considered a global public health problem due to severedisabilities, functional deficiencies and reduced quality of life.Method: The corrective study is assessing the cognitive impairment and the quality of life. The cognitiveimpairment will be measured with the use of the MoCA and QoL will be measured by stroke specific qualityof life questionnaire. Female stroke patient with age in between 45-65 year who was diagnosed by thephysician is included in the study.Discussion: Stroke is a prevalent condition which affects most of the Indian population. Most studies aredone on stroke including male and females both. Many studies have concentrated on cognitive disabilityafter stroke and quality of life in males but no research is available in rural Indian females.The need for theresearch is therefore to establish the prevalence of cognitive disability in females and their effect on qualityof life after stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 teacher head, 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".