Krill oil for knee osteoarthritis: A meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis, a prevalent musculoskeletal disorder, significantly impacts global health and quality of life. Unfortunately, there is no disease modifying osteoarthritis drugs until now. Krill oil is being explored as a potential alternative, however its efficacy in managing knee symptoms remains unclear. Therefore, the meta-analysis of krill oil in knee osteoarthritis would be interesting and useful. METHODS: We conducted a systematic search of PubMed, Cochrane Library, Embase, and Web of Science databases from their inception through November 28, 2024, employing predefined search terms, including "krill oil" and "knee osteoarthritis." We included all relevant randomized controlled trials to ensure a comprehensive analysis. Visual analog scale and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) of pain, stiffness and function were served as primary outcomes. Moreover, blood markers and adverse events were also included. RESULTS: Five randomized controlled trials involving 730 participants were included. Relative to the usual care group, the krill oil group demonstrated no significant improvement in knee osteoarthritis as measured by visual analog scale; however, it exhibited significant benefits in terms of pain (standardized mean difference [SMD] -0.60; 95% confidence interval [CI] -0.99 to -0.21), stiffness (SMD -0.59; 95%CI -1.04 to -0.14), and functional outcomes (SMD -0.68; 95% CI -1.09 to -0.27) based on WOMAC assessments. Analysis of blood markers also revealed no significant effects of krill oil group compared to the usual care group. Moreover, adverse events in the krill oil group and usual care group also showed no statistical difference. The safety profiles were similar between the 2 groups. CONCLUSION: Krill oil presents as a promising safe therapeutic option for knee osteoarthritis; however, its efficacy in pain relief requires further investigation.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.048 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".