First-in-Human Evaluation of a New Resorbable Microspherical Embolic Agent for Genicular Artery Embolization to Treat Pain Secondary to Knee Osteroarthritis
Bibliographic record
Abstract
PURPOSE: To evaluate the safety and effectiveness of genicular artery embolization (GAE) using an resorbable microspherical embolic agent to treat pain secondary to knee osteoarthritis (KOA). MATERIALS AND METHODS: This prospective, single-arm, unblinded, first-in-human study was performed in 15 patients (12 women; mean age, 62.8 years). GAE was performed with 200-μm (SD ± 75) resorbable alginate microspheres (SakuraBead; CrannMed, Galway, Ireland). The primary endpoint was safety, with effectiveness evaluated as a secondary endpoint using the visual analog scale (VAS) pain score and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at baseline, 1 month, 3 months, and 6 months. Adverse events were recorded at all timepoints. RESULTS: GAE was technically successful in all patients. There were 6 adverse events recorded in 6 different patients; all were minor and self-limited (Grade 1). The percentage reduction in VAS, WOMAC total, and WOMAC pain scores was calculated for individual patients. This showed that there was a statistically significant reduction (P < .001) in the mean VAS score of 76%, 77%, and 63% at 1, 3, and 6 months, respectively. There was a 78% reduction in WOMAC total score at both 1 and 3 months and a 76% reduction at 6 months (P < .01); this was accompanied by a reduction in WOMAC pain score of 78%, 81%, and 78% at 1, 3, and 6 months, respectively (P < .01). CONCLUSIONS: In this limited pilot study, GAE using alginate resorbable microspheres was safe and produced significant improvement in pain and function in patients with KOA maintained to 6-month 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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".