Multimodal Pain Management in Knee Osteoarthritis: A Comparative Study of Pregabalin and Duloxetine as Adjuncts to Naproxen
Bibliographic record
Abstract
OBJECTIVE: This single-blinded, randomised, prospective study was carried out to investigate the effectiveness of a combination drug regimen including pregabalin and duloxetine as adjuncts to naproxen in pain management of patients with knee osteoarthritis (OA). METHODS: One hundred and five patients were inducted into the study following the inclusion and exclusion criteria. Using simple randomisation, patients were allocated into group A (naproxen only), group B (naproxen + duloxetine), and group C (naproxen + pregabalin). Visual analogue scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores were used to measure pain severity, while secondary outcomes of sleep quality and depression were assessed by using the Pittsburgh Sleep Quality Index (PSQI) and Beck Depression Inventory (BDI) scale, respectively. Assessments were done at day 0, week 4, and week 12 in the study. Appropriate tests were utilised to analyse the data. RESULTS: Mean pain scores at 12 weeks were significantly lower in group B (p=0.009 for VAS, p=0.002 for WOMAC) and group C (p=0.012 for VAS, p=0.005 for WOMAC), as compared to group A (naproxen only). Sleep quality (p=0.00) and depression scales (p=0.00) were also similarly improved in the combination drug regimen groups as compared to the naproxen-only group. CONCLUSION: Addition of either duloxetine or pregabalin to the naproxen drug regimen may result in better management and improved quality of life in patients suffering from knee OA.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".