Pneumatic Compression versus Local High-Frequency Vibration Impact on Nerve Conduction and Balance Performance in Diabetic Polyneuropathy
Bibliographic record
Abstract
Background: One of the most common microvascular complications of diabetes is neuropathy. Objective: This study aimed to compare the impact of intermittent pneumatic compression (IPC), high-frequency vibration (HFV), and balance program on nerve conduction velocity (NCV) as well as balance performance in patients with diabetic polyneuropathy. Patients and methods: Sixty female patients had lower limb diabetic polyneuropathy (DPN). Their ages ranged from 50 to 60 years. They were randomized into 3 groups of the same number (n=20). The vibration group received plantar, ankle, and cuff HFV. The compression group received lower limbs IPC, and the exercise group received the balance exercise program. The trial was for 12 weeks (3 sessions/week). NCV, Toronto clinical scoring (TCS), and Berg Balance Scale (BBS) were assessed at baseline and after treatment. Result: The study demonstrated that all the three groups (HFV, IPC, and exercise) had significant improvement from pre- to post-treatment, with no significant differences among them. The TCS decreased by 17.8%, 11.2%, and 14.8% respectively, in favor of the HFV group. The percentage of improvement of the BBS was 10.8%, 13.04 and 10.6 % respectively for the IPC group. The HFV and IPC groups showed better motor and sensory NCV improvement than the exercise group. Conclusions: The local HVF andIPC have almost the same positive impact on NCV and different quality-of-life issues for DPN as relieving pain and paresthesia, improving proprioception, and promoting functional balance better than the exercise group.
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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 | 0.000 |
| 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) | 0.002 | 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".