Evaluation of clinical outcomes in neuropathic pain with combinations of anti-neuropathic drugs
Bibliographic record
Abstract
Background: Much of the pharmacological treatment modalities especially individual drugs for treating neuropathic pain have unwanted side effects, multiple day to day dosing, modest efficacy of topical treatments, and their local side effects. Combination drug regimen has the advantage of offering relatively better pain relief at lower drug doses and lesser side effects.Methods: The study was conducted in the Department of Neurology at NRI General Hospital, Guntur. The patients who met the inclusion and exclusion criteria were enrolled and assigned into 3 groups of the study drug combinations. The baseline characteristics and post interventional scores of Toronto Clinical Scoring System (TCSS), visual analogue scale (VAS), Hamilton Anxiety Rating Scale (HAM-A), Hamilton Depression Rating Scale (HAM-D) and Medical outcome of sleep scale (MOS) and were analyzed using t test and mean difference.Results: A statistically significant reduction in neuropathic pain in all the three groups was found. The mean difference between the baseline and post interventional scores of TCSS and VAS of group I, II and III were 2.97, 2.75, and 1.97; 2.32, 1.12, and 0.95 respectively. There was a statistically significant improvement of HAM-A in all the three groups, HAM-D and MOS sleep scale were found significant only in group II.Conclusions: The study findings revealed that all the three drug combinations were effective in the management of neuropathic pain with pregabalin and oxcarbazepine combination being better with respect to efficacy and tolerability. Regarding the treatment of depression and sleep disturbances associated with NP pregabalin and duloxetine was more effective.
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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 | 0.000 |
| 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".