Application of multiple cognitive evaluation scales in follow-up of patients with cognitive impairment after cerebral stroke
Bibliographic record
Abstract
Objective To study the application of multiple cognitive evaluation scales in follow-up of patients with cognitive impairment after cerebral stroke and in multicenter clinical research. Methods Of the 197 patients enrolled into the baseline period of this study from 10 centers of China,who were evaluated using multiple scales such as MMSE,MoCA,CDR,and computer interruptive memory test in the baseline period,and 6 and 12 months after enrollment,154 completed the follow-up.The completed percentages of different scales and scores of different centres were summarized.Trend figures were plotted for the changes in different scales,and difference in different indications during the follow-up period was calculated.Results The completed percentage of different scales was over 95%except for that of the computer interruptive test.No significant difference was found in the scores of different centers and the indexes were improved during the follow-up,indicating that the scales can be used in follow-up of patients with vascular cognitive impairement after cerebral stroke and applied in multicenter clinical research.Conclusion The scales we selected in this study can be used in multicenter clinical research and as the observation indications during the follow-up of patients with cognitive impairment after cerebral stroke.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".