Topical diclofenac in the treatment of osteoarthritis of the knee
Bibliographic record
Abstract
Niklas Schuelert, Fiona A Russell, Jason J McDougallDepartment of Physiology and Pharmacology, University of Calgary, Calgary, AB, CanadaAbstract: Osteoarthritis (OA) is a common disease that affects millions of people worldwide. As there is no cure for OA, drug treatment to relieve symptoms is the main form of management. Nonsteroidal anti-inflammatory drugs (NSAIDs) such as diclofenac are the most commonly used drugs to treat knee OA pain. Unfortunately, these agents are associated with gastrointestinal and cardiovascular risks, which limit their chronic use. Topical NSAIDs are emerging as a viable alternative for managing OA pain. Because a pharmacologically effective dose can be restricted to the site of pain, there is superfluous systemic absorption, and the risk of related adverse effects is minimized. This article reviews the currently available preclinical and clinical information on topical diclofenac for the treatment of OA pain, including data from recently published randomized controlled trials regarding the efficacy and safety of topical diclofenac. Most studies confirm topical diclofenac to be as effective as oral diclofenac with significantly reduced side effects; however, the efficacy of NSAIDs is far from optimal, and more research needs to be done to investigate the underlying mechanisms of OA in order to improve treatment options, especially for patients with NSAID-resistant OA pain.Keywords: osteoarthritis, topical diclofenac, NSAID, joint painErratum for this paper has been published
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".