Bone Marrow Aspirate Concentrate Is More Effective Than Hyaluronic Acid and Autologous Conditioned Serum in the Treatment of Knee Osteoarthritis: A Retrospective Study of 505 Consecutive Patients
Bibliographic record
Abstract
The aim of this study was to evaluate and compare the effectiveness of three different intra-articular injective treatments: hyaluronic acid (HA), autologous conditioned serum (ACS) and bone marrow aspirate concentrate (BMAC) for the treatment of knee osteoarthritis (OA). A Level III retrospective comparative clinical study was performed on 505 consecutive patients treated with HA (n = 171), ACS (n = 222) or BMAC (n = 112) for knee OA. The mean patient age was 52 ± 13 years; 54.5% were males. Collected data included patient demographics, symptoms, visual analogue scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and radiographic classification of osteoarthritis grade using plain radiographs and advanced imaging. Clinical outcome was assessed at 3 and 12 months post treatment. Significant improvement in VAS and WOMAC was seen for all three treatments at the 3-month follow-up. At 12 months, VAS was improved in all three treatment groups, yet only BMAC sustained the improved WOMAC even in patients with more severe degenerative changes. This study shows that BMAC is more effective than HA and ACS in the treatment of symptomatic knee OA, especially in the patients with more severe degenerative changes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".