Specific balance training included in an endurance-resistance exercise program improves postural balance in elderly patients undergoing haemodialysis
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the effects of 6 months of specific balance training included in endurance-resistance program on postural balance in haemodialysis (HD) patients. METHODS: Forty-nine male patients undergoing HD were randomly assigned to an intervention group (balance training included in an endurance-resistance training, n = 26) or a control group (resistance-endurance training only, n = 23). Postural control was assessed using six clinical tests; Timed Up and Go test, Tinetti Mobility Test, Berg Balance Scale, Unipodal Stance test, Mini-Balance Evaluation Systems Test and Activities Balance Confidence scale. RESULTS: All balance measures increased significantly after the period of rehabilitation training in the intervention group. Only the Timed Up and Go, Berg Balance Scale, Mini-Balance Evaluation Systems Test and Activities Balance Confidence scores were improved in the control group. The ranges of change in these tests were greater in the balance training group. CONCLUSIONS: In HD patients, specific balance training included in a usual endurance-resistance training program improves static and dynamic balance better than endurance-resistance training only. Implications for rehabilitation Rehabilitation using exercise in haemodialysis patients improved global mobility and functional abilities. Specific balance training included in usual endurance resistance training program could lead to improved static and dynamic balance.
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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) | 0.001 | 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) | 0.001 | 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".