Effectiveness of Pregabalin Compared to Duloxetine in Diabetic Peripheral Neuropathic Pain: An Observational Study.
Bibliographic record
Abstract
INTRODUCTION: Neuropathy is a comorbid complication of diabetes and Pregabalin and Duloxetine are the two most common drugs used for the treatment of neuropathic pain. AIM: To determine the effectiveness and side effects of Pregabalin and Duloxetine in patients with diabetic peripheral neuropathic pain. MATERIALS AND METHODS: This prospective observational study was conducted at Max Super Speciality Hospital. Patients attending the endocrinology department, above 18 years of age who were prescribed with Pregabalin or Duloxetine were screened and included in this study. The data was collected for all study participants using a specially designed case record form by conducting personal interviews. SF-MPQ, Mc-Gill, NRS and DN-4 questionnaires were used to assess the extent of pain and the side-effects associated with the drugs. RESULTS: Based on the responses from the Numerical Rating Scale and McGill Pain Questionnaire, Pregabalin was seen to be less effective compared to Duloxetine. The only side effect observed with Pregabalin was drowsiness, which was observed in 4% cases at 50 mg dose whereas those reported with Duloxetine were drowsiness (22.2% at 20 mg and 33.3% at 30 mg), vomiting (11.1% at 20mg and 30mg), headache (11.1% at 20 mg and 30 mg), and dizziness (0% at 20mg and 11.1% at 30 mg). CONCLUSION: Pregabalin has a better safety profile and tolerability compared to Duloxetine but the latter is more effective in treating Diabetic Peripheral Neuropathic Pain. However, further studies with a larger sample size and longer duration are required to be conducted for finding the effectiveness of these drugs, specifically in the Indian population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".