Panchakarma management of Diabetic Peripheral Neuropathy - A Case Report
Bibliographic record
Abstract
Background: Diabetic peripheral neuropathy is a type of nerve damage that can occur with uncontrolled diabetes. It affects 50% to 90% of patients and out of these 15-30% will have painful diabetic neuropathy. Conventional medicine has less than satisfactory result, hence there is need to find out safer & effective treatment from the sources other than the conventional medicine. Aim: The aim was to evaluate the role of Panchakarma treatment modalities in Diabetic peripheral neuropathy. Method: A 60-year-old female patient suffering from Type 2 Diabetes mellitus for 5 years, then she gradually developed Tingling sensation over bilateral lower limb, burning sensation, pain, sense of numbness, constipation for 3 years. There was no any Straightforward correlation with Ayurveda so treatment was done as per associated Dosha condition. Panchakarma procedure such as Nitya Virechana with Castor oil and Milk was given as per Agni and Kostha of the patient, Utsadana with Triphala-Nimba Churna with Nimba Taila and Dashamoola Ksheera Parisheka was done for 15 days. Assessment was done with Fasting and post-prandial blood sugar level, MTCNS (Modified Toronto clinical neuropathy score), Vibration perception threshold, Hot threshold, Cold threshold, Mono filament test was done by Neuropathy analyzer machine before and after completion of treatment. Result & Conclusion: Significant improvement was seen in subjective as well as objective parameters after the treatment. The study suggested that Nitya Virechana along with Bahirparimarjana Chikitsa can be useful to relieve the symptoms of Diabetic peripheral neuropathy.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".