Clinical effects of two different doses of duloxetine compared to conventional analgesic therapy in patients with osteoarthritis knee
Bibliographic record
Abstract
Background: Pain is the leading symptom of knee osteoarthritis (OA) leading to significant morbidity and decreased quality of life. Duloxetine, a selective serotonin norepinephrine reuptake inhibitor, has been demonstrated to have a centrally acting analgesic effect. Aims and Objectives: To evaluate the efficacy and safety of two different doses of duloxetine and compare with conventional pharmacotherapy in treatment of chronic pain due to osteoarthritis of knee. Materials and Methods: 90 patients with symptomatic knee OA were randomly divided into 3 groups to receive duloxetine 40 mg & 3g paracetamol/day (Group A), duloxetine 20 mg & 3g paracetamol/day (Group B) and paracetamol 3gm/day (Group C). Patients were followed up for 6 months to assess pain relief and functional improvement. Visual Analogue Scale (VAS) for assessing pain intensity and Western Ontario and McMaster Universities Arthritis Index (WOMAC) questionnaire physical function subscale for assessing physical function were used. Results: Reduction in VAS score from baseline was significantly high in groups A and B as compared to C at 1 month, 3 months and 6 months. Reduction in WOMAC score from baseline were also significantly high in groups A and B as compared to C at 1 month, 3 month and 6 months. Adverse effects in Group A were significantly high as compared to group B and C. Patients discontinuing due to adverse effects were significantly high in group A. Conclusion: Lower dose of duloxetine is associated with significant pain reduction and improved function with lesser adverse effects in patients with pain due to 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".