The efficacy of transcatheter arterial embolization for knee pain on patients with knee osteoarthritis: A case series
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (OA) is accompanied by inflammation and angiogenesis. Modifying angiogenesis through transcatheter arterial embolization (TAE) can be a potential treatment for knee OA. OBJECTIVE: We subjected five OA knees in three patients to TAE and report the results of our post-treatment observations. CASE DESCRIPTION: Three patients that had experienced knee pain for a minimum of one year prior to the study, and whose pain had persisted despite conservative treatment, were included in this study. Patients more often chose conservative treatment over surgical treatment. Pain and functional scales were evaluated before, immediately, and 1 month after TAE using the Numeric Rating Scale (NRS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). TAE was performed by an experienced interventional radiologist. The average values of NRS evaluated before and after 5 TAEs were 5.2 before TAE, 3 immediately after TAE, and 3.6 after 1 month of TAE, and the average values of WOMAC were 52, 38.4, and 36.4, respectively. There were no major adverse effects. CONCLUSION: The examined cases support the conclusion that TAE is an effective treatment for patients with knee OA. Substantial pain relief and WOMAC improvement were observed both immediately and one month after TAE.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".