An Ayurveda Management of Diabetic Peripheral Neuropathy: A Vibrotherm-Based Assessment of Treatment Efficacy
Bibliographic record
Abstract
Diabetic Neuropathy (DN) is a peripheral nerve disorder in individuals with diabetes, characterized by neuropathic pain, which can lead to severe complications if untreated. Conventional treatments often offer only short-term relief and carry side effects, prompting exploration of alternative therapies. A 59-year-old woman with poorly controlled type 2 diabetes, COPD, and low bone density presented with acute low back pain, radiating to the legs, and numbness in both palms and left leg. Despite allopathic treatment, her symptoms persisted, leading her to seek Ayurvedic care. Clinical examination revealed signs of diabetic peripheral neuropathy, osteopenia, and degenerative spine disease. Ayurvedic intervention focused on balancing Vata and Pitta doshas through therapies like Padabhyangam, Lepam, Dhanyamla dhara, Jambeerapinda swedam, Patrapota swedam, and Shastika pinda swedam, combined with internal medications such as Nishakatakadi kasayam, Pramehoushadi, Rasna Saptakam Kashayam, Kaisora Guggulu, Ksheerabala 101 cap, and Dhanwantharam Gritam. Assessment was conducted using Vibrotherm, Diabetic Neuropathy Examination (DNE), Michigan Neuropathy Screening Instrument (MNSI), and Toronto Clinical Neuropathy Score (TCNS). Post-treatment assessments showed significant improvement in symptoms, including reduced pain, numbness, and burning sensations. Objective measures using various neuropathy assessment tools indicated decreased neuropathy severity. The Ayurvedic treatment demonstrated efficacy in managing diabetic neuropathy symptoms, offering a viable alternative to conventional therapies. Early diagnosis and integrated Ayurvedic care can provide substantial symptom relief and improve the quality of life for diabetic neuropathy patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".