At the Edge of Orthopaedics: Initial Experience with Transarterial Periarticular Embolization for Knee Osteoarthritis in a Romanian Population
Bibliographic record
Abstract
Background: Transarterial embolization (TAE) of genicular artery branches is a relatively new technique that has emerged as a promising method for delaying invasive knee surgery in patients suffering from degenerative knee osteoarthritis (OA). In mild to moderate OA, invasive major surgery can be safely postponed, and patients with major risk factors now have an alternative. Our aim was to examine the impact of TAE on clinical outcomes in individuals with degenerative knee OA over a 12-month period. Methods: A case series of 17 patients diagnosed with knee OA and treated with TAE was included in the study. Every patient was clinically evaluated at different timeframes according to the Western Ontario and McMaster Universities’ arthritis index, knee injury, and osteoarthritis outcome scores, and the 36-item short-form survey (WOMAC, KOOS, and SF-36). Results: At the first follow-up (1 month), KOOS and WOMAC improved from 46.6 ± 13.2 (range 27.3–78.2) to 56.5 ± 13.9 (range 32.3–78.4; p = 0.023) and 49.5 ± 13.2 (range 29.3–82.3) to 59.8 ± 12.6 (range 39.3–83.5, p = 0.018), respectively. Physical SF-36 improved significantly from 42.1 ± 7.75 (range 30.3–57.3) to 50.5 ± 9.9 (range 35.6–67.9; p = 0.032). No significant changes in scores were observed at three, six, or twelve months after TAE. Conclusions: TAE provided early pain reduction and considerable improvement in quality of life without complications for a consecutive sample of Romanian patients with mild to severe knee OA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".