Pharmacological Use of Gamma-Aminobutyric Acid Derivatives in Osteoarthritis Pain Management: A Systematic Review
Bibliographic record
Abstract
Abstract Background Pain is the major complication of osteoarthritis (OA) patients and is a decisive symptom for medical intervention. Gamma-aminobutyric acid (GABA) derivatives are optional painkillers but not widely used in pain management of OA patients. We synthesized the efficacy and safety of GABA derivatives for OA pain management. Methods We searched Medline, Cochrane CENTRAL, Embase, and ClinicalTrals.gov from inception to 13 October 2021 and included randomized controlled trials (RCTs) comparing the efficacy and safety of GABA derivatives with placebo or standard control in OA pain management. Two independent reviewers extracted data and assessed these studies for risk of bias using Cochrane Collaboration’s tool for RCT. Results In total, three eligible RCTs (n=3) meeting the eligibility criteria were included. Among these RCTs, one focused on hand OA pain management, while two RCTs focused on knee OA. In hand OA, pregabalin reduced numerical rating scale (NRS) score and the Australian/Canadian Osteoarthritis Hand Index (AUSCAN) pain score significantly compared with placebo, and caused 55 AEs. In knee OA, pregabalin reduced visual analogue scale (VAS) score and the Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain score significantly with no recorded adverse event (AE). Meanwhile, in knee OA, gabapentin reduced both VAS score and WOMAC pain score compared with acetaminophen and caused 9 AEs. Conclusions GABA derivates are effective and safe in OA pain management. However, future researches with large sample size are needed to further prove the efficacy of GABA derivates in OA pain control. Trial registration: CRD42021240225.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".