Effects of task-specific training on motor activity, cognitive function, and quality of life among individuals with Parkinson’s disease: a quasi-experimental pilot study
Bibliographic record
Abstract
Introduction The second most prevalent neurological condition in adults over 50 years is Parkinson’s disease (PD). Individuals with PD(IwPD) experience motor and non-motor symptoms during disease progression. One of the most significant variables in non-motor symptoms is cognitive impairment, andthe disease may lead to dementia. Even though the cognitive impairment in IwPD is mild, there should be concern regarding its rehabilitation because there is no pharmacological management for cognitive impairment, and just a handful of studies on functional cognitive training for IwPD have been published. This raises the prospect of task-specific training among IwPD and functional rehabilitation of cognitive function. Methods Thisquasi-experimental study involved 30 participants, who were assessed and selected based on inclusion and exclusion criteria. Pre-test and post-test values were obtained using the Montreal Cognitive Assessment (MoCA), Unified Parkinson’s Disease Rating Scale (UPDRS) Part III, and Parkinson’s Disease Questionnaire (PDQ)-39. All subjects underwent task-specific training with cognitive training for 8 weeks. Results Statistical analysisshowed significant improvements in motor activity, cognitive function, and quality of life. The p-value of each outcome measure was < 0.0001 after analysingpre-test and post-test data. Conclusions According to the findings ofthis study, task-specific training combined with cognitive training significantly improved motor activity, cognitive function, and quality of life among IwPD.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".