Development and validation of ‘Cognitive Assessment Scale for Stroke Survivors’
Bibliographic record
Abstract
Background & Objectives: Stroke is a leading cause of death and it causes significant long-term disabilities. It affects cognition and physical impairment in the patients. Cognitive impairments caused by stroke include loss of memory, disorientation, impaired attention, reasoning, and social perception. It may also lead to interaction deficit and inability to problem-solving, etc. The precise knowledge about the degree of cognitive impairment is essential to address the issue with appropriate measures. We aimed to develop a cognitive measurement scale for stroke patients. Methodology: The phenomenon was explored through in-depth interviews of 12 stroke survivors in different hospitals in Lahore, Pakistan. Seventeen items were generated. After factor analysis, 15 items were included in the scale and a pilot study was conducted on 15 participants. A sample of 106 patients was selected to administer the scale Cognitive Assessment Scale for Stroke Survivors (CASS) and Mini-Mental State Examination (MMSE) scale for concurrent validity. Results: The Principal Component Factor Analysis through Varimax rotation yielded three factors, e.g., ‘Social Cognition’, ‘Focus and Attention’, and ‘Orientation’. The results have shown significant values with good psychometric properties. The Cronbach’s Alpha value of the developed scale is 0.88 which indicates it as a highly reliable scale. Conclusion: This research reported that stroke survivors experience cognitive impairment after the stroke incidents. The developed scale to measure cognitive impairment after a stroke incident was proved to be valid and reliable, and can be used in medical practice. Abbreviations: CASS – Cognitive Assessment Scale for Stroke Survivors; MMSE – Mini-Mental State Examination. Citation: Qamar S, Iqbal MN, Rafiq M, Ismat Ullah Cheema IU, Masood K. Development and validation of ‘Cognitive Assessment Scale for Stroke Survivors’. Anaesth. pain intensive care 2022;26(4):663-668. DOI: 10.35975/apic.v26i4.2025
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".