Efficiency of alpha-lipoic acid and physical therapy in patients with diabetes polyuropathy
Bibliographic record
Abstract
Diabetic polyneuropathy is the most common microvascular complication of diabetes. Alpha-lipoic acid and physical therapy are used in the treatment of this progressive disease. Aim of this study was to assess the efficacy of combined use of alpha-lipoic acid and physical therapy in patients with diabetic polyneuropathy and identify the factors which lead to a better therapeutic response. The study included 95 patients with diabetic polyneuropathy who were parenterally treated with alpha-lipoic acid in combination with the kinesiotherapy, vacuum compression therapy, carbon dioxide therapy and galvanic baths. The Neuropathy Total Symptom Score-4-TSS-4 and the Toronto Clinical Scoring System (Toronto CSS) were used to evaluate the effectiveness of the therapy. After the applied therapeutic procedures, the values of both TSS-4 and Toronto CSS statistically significantly decreased (p <0.01). The duration of diabetes, duration of symptoms of polyneuropathy, the level of glycemic control and body mass index were not significantly correlated with the degree of reduction in TSS-4 (p> 0.05), while we found a statistically significant negative correlation between the value of TSS-4 before treatment and the degree of its reduction (p <0.05). The combined use of alpha-lipoic acid and physical therapy is effective in reducing subjective symptoms and signs of diabetic polineuropathy. Therapeutic response is better if symptoms of neuropathy are less pronounced. Long-term studies of the efficacy of alpha-lipoic acid and physical therapy are needed, as well as the identification of factors that could affect better therapeutic response.
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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".