A Case Study of Balance Rehabilitation in Parkinson's Disease
Bibliographic record
Abstract
There is evidence to say that balance can be improved by manipulating peripheral sensory inputs. Our hypothesis was, improvement in the inputs from proprioceptors using sensory specific balance training would improve balance. We intend to document the influence of training proprioceptors in improving balance among Parkinsonism. Single case study of a 65 years old parkinson's subject was considered. Trial was designed as a 4 week balance training program. Outcome measures were Berg's balance scale, Multidirectional reach test and CTSIB. Balance was trained by making the subject perform balance exercises standing over a square foam surface which reduces the quality of surface orientation input. Training was given for 15-20 mins/day, 5 days in a week, for a period of 1 month. We observed a 25% increase in values of FFR, BFR, LFR & RFR for multi directional reach test. Overall Berg's balance score improved from 48 to 54. CTSIB assessed before the training showed a poor performance in conditions 5 & 6, post training assessment showed an improvement of 12 seconds for condition 5 and 11 seconds for condition 6. The results suggest that sensory-specific balance exercise has a positive training effect on balance among subjects with Parkinsonism.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".