Autologous Biologic Treatment with Fat, Bone Marrow Aspirate and Platelet Rich Plasma Is an Effective Alternative to Total Knee Arthroplasty for Patients with Moderate Knee Arthrosis
Bibliographic record
Abstract
Background: Osteoarthrosis (OA) of the knee afflicts millions worldwide. Total Knee Arthroplasty (TKA) is common, but associated with substantial cost and morbidity. Prior studies of intra-articular injection of fat, bone marrow aspirate (BMA), and platelet rich plasma (PRP) have shown clinical benefit. We hypothesized that injection of autologous adipose tissue, BMA, and PRP would provide significant benefit for patients with moderate knee OA resulting in avoidance of total knee arthroplasty (TKA) in most, with discontinuance of NSAIDs and other drugs. Methods: 42 TKA candidate patients (47 knees) with moderate (Kellgren-Lawrence 2 and 3) knee OA who had failed conservative treatment had autologous adipose tissue, BMA, and PRP injection as an alternative to TKA in office using only local anesthetic. Patients had discontinuance of all nonsteroidal anti-inflammatory medicines (NSAIDs) and other analgesics, except acetaminophen, prior to treatment. Patients were evaluated with Knee injury and Osteoarthritis Outcome Score Physical Shortform (KOOS-PS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Single Assessment Numeric Evaluation (SANE) prior to treatment, and at 6 months, 1, and 2 years after treatment. Results: Follow up exceeded 80% at all time points. There were no significant adverse events. TKA was avoided in 97% at one and 86% at two years after treatment. Mean SANE, KOOS-PS, and WOMAC scores significantly improved at 6 months, 1, and 2 years post-treatment. WOMAC and SANE scores were higher at two versus one year post-treatment. Conclusions: Combined fat, BMA, and PRP injection is a safe and effective treatment for moderate knee OA, with reliable avoidance of TKA and possible continued improvement at two year follow-up.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".