Intraarticular gold microparticles using hyaluronic acid as the carrier for hip osteoarthritis. A 2-year follow-up pilot study
Bibliographic record
Abstract
We recently conducted a pilot study in which we discovered that introducing metallic gold microparticles into the knee joint can help reduce pain caused by osteoarthritis for up to two years. Additionally, we identified significant proteomic changes in the synovial fluid and serum within eight weeks of the procedure. In this study, we aimed to evaluate whether there may be a clinical relevant effect of intra-articular injection of gold microparticles on hip osteoarthritis. A cohort of 22 patients, aged ≥ 18 years, with pain ≥ 3 months, and Kellgren-Lawrence OA grade 2-4, were included. Metallic gold 20 mg, 72.000 pieces, 20-40 µ-meter BerlockMicroImplants (BMI) were injected into the hip joint using hyaluronic acid as the carrier. In total, we treated 26 hip joints. The primary outcome was the Western Ontario and McMaster Universities Arthritis Index (WOMAC). WOMAC pain decreased from 11 (2-20 to 3 (0-8), stiffness from 6 (0-8) to 1 (0-4), and activity from 43 (18-68) to 11 (0-27), all P = 0.0001. When adjusting for the minimally relevant differences, the P-values were 0.0015 for WOMAC pain, 0.26 for stiffness, and 0.011 for activity. Combined intraarticular treatment with metallic gold microparticles and hyaluronic acid may improve hip joint pain and function. Joint stiffness did not improve when assessed against the minimal clinically relevant difference. This study suggests a basis for a future placebo-controlled randomized trial of gold microparticles and hyaluronic acid in hip osteoarthritic patients.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".