Genicular Artery Embolization Using Mesenchymal Stem Cells for the Treatment of Knee Osteoarthritis: A Prospective Study
Bibliographic record
Abstract
PURPOSE: To assess the feasibility and safety of genicular artery embolization (GAE) using mesenchymal stem cells in patients with bilateral knee osteoarthritis (OA). MATERIALS AND METHODS: Thirty patients diagnosed with Kellgren-Lawrence Stage 3 bilateral knee OA were enrolled. GAE was performed unilaterally in each subject using 70 million cells per injection (26 descending genicular arteries, 2 inferior medial genicular arteries, and 2 superior medial genicular arteries). Technical success was defined by successful delivery of cells. Safety was evaluated by documentation of procedure-related or subsequent adverse events. Clinical outcomes were assessed with the visual analog scale and Western Ontario and McMaster Universities Osteoarthritis Index before treatment and during the subsequent 12 months. Magnetic resonance (MR) imaging was performed preprocedurally and 3 months postprocedurally. The untreated knee served as an internal control for each subject. RESULTS: Technical success was achieved in 100% of cases. There were two Society of Interventional Radiology (SIR) Grade 1 procedure-related adverse events (temporary skin discoloration). There were no adverse events during the study follow-up period. Significant improvements were observed in Western Ontario and McMaster Universities Osteoarthritis Index scores (29.6 [SD ± 11.5] to 4.6 [SD ± 4.5] at 12 months; P < .001) and visual analog scale scores (6.8 [SD ± 1.6] to 2.0 [SD ± 1.5] at 12 months; P < .001). CONCLUSIONS: GAE was safely performed using mesenchymal stem cells. The mechanism of action and durability of response remain uncertain.
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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 | 0.000 | 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".