Comparison of the effects of duloxetine and pregabalin on pain and associated factors in patients with knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: This study aimed to investigate the effects of duloxetine and pregabalin primarily on pain and functional status in patients with knee osteoarthritis and secondarily on quality of life, depression, anxiety, and sleep disturbance. METHODS: A total of 66 patients with knee osteoarthritis were randomized to use duloxetine or pregabalin. Patients were evaluated by Visual Analog Scale, Neuropathic Pain Diagnostic Questionnaire, Western Ontario and McMaster University Osteoarthritis Index, Short Form-36, Beck Depression Inventory, Beck Anxiety Inventory, and Pittsburg Sleep Quality Index before the treatment and after 4 and 12 weeks of treatment. RESULTS: Improvements occurred in Visual Analog Scale, Neuropathic Pain Diagnostic Questionnaire, Western Ontario and McMaster University Osteoarthritis Index, Short Form-36 (with an exception of the mental health subgroup scores in duloxetine-treated group), Beck Depression Inventory, and Beck Anxiety Inventory scores in both groups from 4 weeks after baseline. Pittsburg Sleep Quality Index total scores and SF-36 mental health subgroup scores started to improve on the 4th and 12th weeks in pregabalin- and duloxetine-treated groups, respectively. CONCLUSION: Osteoarthritis pain, a complex outcome with nociceptive and neuropathic components, leads to central sensitization in a chronic phase. Using centrally acting drugs in the control of pain and associated symptoms would increase the probability of treatment success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".