Study on the cognitive dysfunction rehabilitation therapy in 37 elderly patients with stroke
Bibliographic record
Abstract
Objective: To discuss the impact of cognitive rehabilitation on the cognitive function and activities of daily life (ADL) of elderly patients with stroke. Methods: Thirty seven hospitalized patients were randomly divided into either a study group (19 patients) or a control group (18 patients). The study group was treated with Yizhi (dual benefits Ping plus cognitive rehabilitation while the control group was treated only with Yizhi. Meanwhile, all patients received a conventional treatment including cerebral vascular and sports rehabilitation for 8 weeks. The cognitive function and ADL were evaluated by Mini-Mental State Examination (MMSE) and Barthel index, respectively. Results: The between-group difference was significant in MMSE scores before and after treatment ( P0.001). The orientation, memory, attention, calculation and language ability of patients in the study group have been enhanced (P0.05), and all the indications mentioned above accept for recollection and language ability in the control group have been improved (P0.05). The between-group difference before and after treatment is significant in the Barthel index (P0.001). Conclusion: Cognitive rehabilitation can effectively improve the cognitive function and ADL of elderly patients with stroke . Early intervention of cognitive rehabilitation is very important on functional status in patients with good prognosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".