Genicular Artery Embolization: A Promising Treatment Option for Recurrent Effusion Following Total Knee Arthroplasty
Bibliographic record
Abstract
Background: Selective genicular artery embolization (GAE) has shown promise as a minimally invasive treatment option for persistent symptomatic recurrent effusions (REs) following total knee arthroplasty (TKA). Purpose: We sought to investigate the radiographic and clinical success of GAE for RE after TKA. Methods: We performed a retrospective review of prospectively collected data on primary and revision TKA patients with RE, both hemorrhagic and non-hemorrhagic, who underwent GAE between 2019 and 2021 with a minimum of 6-month follow-up. All embolization procedures were performed by a single interventional radiologist. Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and visual analog scale (VAS) scores were collected prior to GAE and at 1, 3, and 6 months post-procedure. Recurrence of effusion following GAE was assessed at 6 months using ultrasound. Results: Seventeen patients, 10 female and 7 male, with 18 TKAs and a mean (SD) age of 63.1 (8.6) years were included. We saw a mean (SD) of 36.1 (24.4) and 3.3 (3.0) point improvement in WOMAC and VAS scores, respectively. In addition, 14 of the 18 TKAs (77.8%) seen at final follow-up had complete resolution of effusion confirmed by ultrasound. Conclusion: Our retrospective review found that a majority of patients showed significant clinical improvement and resolution of effusion following GAE. These findings suggest that GAE may be an effective minimally invasive treatment option for RE following TKA and should be further investigated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".