Treadmill Training to Decrease Gait Variability in Patients with Parkinson’s Disease
Bibliographic record
Abstract
Introduction: Among neurological diseases, Parkinson’s disease is one of the fastest growing in Canada. With an aging population, the number of diagnoses made continues to increase on both a daily and yearly basis. Among the many symptoms of Parkinson’s disease, gait disturbances are one of the most common with the unintended consequence of falls. These gait disturbances result from a loss of dopaminergic innervation of the basal ganglia, leading to slow and variable gait rhythm. Purpose: Treadmill training has been demonstrated to alleviate gait disturbances for patients with Parkinson’s disease by creating an external stimulus for gait patterns and rhythmicity. With an increased risk of falls among patients with Parkinson’s comes increased stress on the Canadian healthcare system as well as families and/or caregivers. Improvement of gait can reduce the incidence of falls in an aging population thus reducing injuries such as fracture or sprains. Recommendation: Gait disturbances, more specifically gait variability, is among the many parameters of gait that treadmill training has improved. The speed of a patient’s gait is a significant factor in gait variability and increased risk of falling. By implementing treadmill training into the lives of patients with Parkinson’s disease, improved walking speeds, a decrease in gait disturbances and risks of falling can be some of the many positive benefits for both the patient and their caregiver.
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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) | 0.000 | 0.000 |
| 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) | 0.006 | 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".