Exploration of the application of Picture-Based Memory Impairment Screen in stroke patients in a preliminary study
Bibliographic record
Abstract
Objective The objective was to explore the validity and reliability of the Picture-Based Memory Impairment Screen (PMIS) assessment tool in stroke patients and to provide an objective basis for its application in China.Methods: A total of 30 stroke patients in the Department of Rehabilitation Medicine were assessed using the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the PMIS. The results were evaluated by content validity, simultaneous validity, consistency test of instruments of assessment, inter-scorer reliability, and retest reliability.Results: The correlation coefficient between each item score and total score of PMIS was between 0.422 and 0.778 (p < 0.05), showing good content validity. The total score of PMIS was moderately positively correlated with the MMSE short-term memory score (p < 0.001), highly positively correlated with the MMSE long-term memory score and retrospective memory score (p < 0.001), and highly positively correlated with the MoCA long-term memory score, memory index and total score (p < 0.001), indicating good criterion validity. The consistency test of the two instruments of assessment showed that a PMIS ≤ 5 was used as the demarcation score for dementia, and it was tested for consistency with the MMSE dementia score of stroke patients, and the Kappa value was 0.81 (p < 0.001). The inter-scorer reliability and retest reliability were good (inter-scorer reliability intra-group correlation coefficient (ICC) >0.95; retest reliability ICC 0.904).Conclusion: The PMIS was a reliable and valid assessment tool, which can be used as memory impairment screening tool for stroke patients in China.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".