Prognostication of Three-Month Genicular Artery Embolization Outcomes Using Pre-Procedural MRIs
Bibliographic record
Abstract
PURPOSE: To assess the utility of pre-procedural knee MRI prior to genicular artery embolization (GAE) for the prognostication of early outcomes for symptomatic knee osteoarthritis (KOA). MATERIALS AND METHODS: A single-center study including 39 patients received GAE from 9/2021-4/2025 with pre-procedural MRIs. MRIs were evaluated for structural abnormalities including menisci, ligaments, cartilage, marrow signal, and loose bodies. For 24 patients, synovitis was assessed using a semiquantitative method. Clinical outcomes were measured with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scoring system at pre-GAE and three-month postintervention. Categorical response was assessed as a 50% reduction in the WOMAC Pain. Student t-tests were used to evaluate WOMAC pain reduction, and subset analysis was performed between MRI structural abnormalities and categorical response. RESULTS: Fifty-two knees received GAE with pre-procedural MRIs (33 with contrast). There was a 34.6% clinical response rate (N = 8/52). Lateral meniscus and cartilage abnormality were predictive of poor categorical response at three months (P = 0.039-0.040). ≥ 4 structural abnormalities were associated with poor treatment response (P = 0.004). Pre-GAE synovitis was not predictive of categorical response at three months (P = 0.809). Kellgren-Lawrence ≥ 3 was predictive of poor response (P < 0.001). Lower adverse event rate was observed with temporary embolic compared to permanent embolic (P = 0.032). CONCLUSION: Pre-GAE knee MRI may offer short-term prognostic utility. Pre-procedural abnormalities in the lateral menisci and cartilage can predict poor response to GAE at three months. A greater degree of structural abnormality (≥ 4 structural abnormalities) was associated with poor response. Temporary embolic agent may be safer than permanent embolic agent.
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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 | 0.003 |
| 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) | 0.001 | 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".