Effect of Early Cognitive Training Combined with Aerobic Exercise on Quality of Life and Cognitive Function Recovery of Patients with Poststroke Cognitive Impairment
Post-publication record
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Bibliographic record
Abstract
Objective. To explore the effect of early cognitive training combined with aerobic exercise on quality of life (QOL) and cognitive function recovery of patients with poststroke cognitive impairment (PSCI). Methods. Ninety PSCI patients treated in our hospital from April 2019 to April 2020 were selected as the subjects and were divided into the experimental group (EG) and control group (CG) according to the admission order, with 45 cases each. Patients in CG received conventional health education combined with rehabilitation training, and those in EG accepted early cognitive training combined with aerobic exercise so as to evaluate the clinical effect of different intervention modes on PSCI patients. Results. Compared with CG after intervention, EG obtained an obviously higher Stroke Specific Quality of Life scale (SS-QOL) score, Montreal Cognitive Assessment (MoCA) score, Barthel Index (MBI) (BI) score and Functional Independence Measure (FIM) score (P < 0.001), and obviously shorter time for completing TMT-A and TMT-B (P < 0.001). Conclusion. Performing early cognitive training combined with aerobic exercise for PSCI patients can effectively improve their QOL and promote the recovery of cognitive function. Compared with conventional health education combined with rehabilitation training, this mode presents a higher application value. Further study will be conducive to establishing a better solution for 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".