Effect of Sensory Re-education in Combination with Balance Training on Fall Risk and Quality of Life in Elderly Diabetic Peripheral Neuropathy Patients: A Pre- and Postclinical Trial
Bibliographic record
Abstract
Background: Diabetic peripheral neuropathy (DPN) being a complication of diabetes affects balance and causes sensory impairment which links to deterioration of quality of life. As the age advances the risk of fall due to DPN also increases, and hence, the aim of this study was to determine the effect of sensory re-education in combination with balance training on fall risk and quality of life in elderly DPN patients. Methodology: This study includes 27 participants meeting the inclusion criteria which include 60–75 of age, who can ambulate with or without assistance and modified Toronto clinical scoring scale ≥6–8. Balance was assessed using the Berg Balance Scale, Dynamic Gait Index, and quality of life by EuroQol-5Dimension-5 Level (EQ-5D-5 L), respectively. Those who had diabetic foot ulcers and other neurological impairments were excluded from the study. Results: There was significant improvement in balance quality of life and reduction in fall risk ( P = 0.001 <0.05) after 4 weeks of treatment. Conclusion: A significant improvement in balance, quality of life, and reduction of fall risk is seen in older adults with DPN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".