Introduction to cognitive assessment scales in screening for mild cognitive impairment after stroke: A review
Bibliographic record
Abstract
Objective: In order to improve the quality of life of patients more effectively, this paper introduces the types and usages of scales for screening mild cognitive dysfunction after stroke and provides a basis for early identification of mild cognitive impairment. Methods: Read, analyze, summarize, and sort out relevant literature. Results: The mild cognitive impairment assessment scales are broadly divided into two categories: the comprehensive rating scales and the special assessment scales. There are 7 comprehensive assessment scales for the comprehensive rating scales, among which the Mini-mental State of Examination and the Montreal Cognitive Assessment Scale are the most widely used. The special assessment scales are mainly evaluated by the symptoms of cognitive dysfunction and can be divided into five types. Conclusion: Early diagnosis and intervention in patients with cognitive dysfunction will help improve the prognosis of patients. Each assessment scale has its advantages and limitations in both sensitivity and discrimination. Effective use of appropriate scales to diagnose cognitive dysfunction and to screen early, to prevent early, to treat early can effectively improve the quality of life of elderly 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".