Effect of proprioceptive training combined with regular aerobic exercise on motor function in patients with Parkinson’s disease
Bibliographic record
Abstract
[Objective] To investigate the effects of proprioceptive training combined with regular aerobic exercise on motor symptoms, cognitive function and serum BDNF level in patients with Parkinson’s disease. [Methods] A total of 120 patients with Parkinson’s disease admitted to the Third Medical Center of Chinese PLA General Hospital from January 2023 to December 2023 were selected and divided into control group (n=60) and observation group (n=60) according to simple random method(single blind method). The control group only received routine intervention, and the observation group received proprioceptive training combined with regular aerobic exercise. Motor symptoms, cognitive function, serum BDNF level were assessed before and after intervention. [Results] The differences of TUGT time, Berg Balance Scale (BBS) score, length of movement track of pressure center and ellipse area of pressure center in observation group before and after intervention were greater than those in control group (P<0.05). After intervention, Montreal Cognitive Function Assessment Scale (MoCA) score, Mini-mental State Examination Scale (MMSE) score and difference of observation group were higher than those of control group (P<0.05). After intervention, the level and difference of BDNF in observation group were higher than those in control group (P<0.05). [Conclusion] Proprioceptive training combined with regular aerobic exercise can significantly improve motor symptoms and cognitive function in patients with Parkinson’s disease, and the mechanism may be related to the promotion of BDNF level.
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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) | 0.001 | 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".