Efficacy of ultramicronised diclofenac in patients with osteoarthritis - systematic review with network meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: This systematic review with network meta-analysis was performed to compare the effectiveness of oral anti-inflammatory drugs used in Brazil for osteoarthritis. PATIENTS AND METHODS: Randomized clinical trials evaluating ultramicronised diclofenac, diclofenac, celecoxib, etodolac and placebo in patients with osteoarthritis were identified. A search was conducted in May 2021 through PubMed, Scopus and Web of Science databases. A network meta-analysis was developed for efficacy outcome related to analgesia measured by the pain subscale of the Western Ontario and McMaster Universities tool. In addition, surface under the cumulative ranking was performed to rank the drugs in relation to this outcome. RESULTS: Twelve randomized clinical trials were included. Overall, ultramicronised diclofenac 105 mg/day (UD105) was better than all the others, including ultramicronised diclofenac 70 mg/day (UD70). In addition, surface under the cumulative ranking resulted in the following order: 1) ultramicronised diclofenac 105 mg/day (100%), 2) ultramicronised diclofenac 70 mg/day (80%), 3) celecoxib 200 mg/day (49%), 4) diclofenac 100 mg/day (48%), 5) placebo (19%) and 6) diclofenac 150 mg/day (6%). CONCLUSIONS: Ultramicronised diclofenac demonstrated superior efficacy compared to other conventional anti-inflammatory drugs and placebo in relieving osteoarthritis pain.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".