Treatment of Popliteal (Baker) Cysts With Ultrasound-Guided Aspiration, Fenestration, and Injection
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine the efficacy of ultrasound-guided aspiration, fenestration, and injection as a treatment in patients with symptomatic popliteal cysts. HYPOTHESIS: Ultrasound-guided aspiration, fenestration, and injection (UGAFI) is an effective and safe treatment option for symptomatic popliteal cysts. STUDY DESIGN: Retrospective cohort study. LEVEL OF EVIDENCE: Level 3. METHODS: Patients who received a UGAFI of popliteal cysts from 2008 to 2011 were identified. Preaspiration (PA) and follow-up Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, cyst recurrence, complications, cyst complexity, and size were obtained and compared for statistical significance. UGAFI involved aspiration of fluid through a spinal needle, fenestration of the cyst walls and septations, and injection of 1 mL (40 mg) triamcinolone (Kenalog) and 2 mL 0.5% bupivacaine (Sensorcaine) into the decompressed remnant. RESULTS: The mean PA WOMAC score (48.55) improved significantly at final follow-up (FFU) to 17.15 (P < 0.0001) for 47 patients. Within the WOMAC subcategories, there was also a significant difference in pain (PA, 10.68; FFU, 3.94; P < 0.0001), stiffness (PA, 4.51; FFU, 1.77; P < 0.0001), and physical function (PA, 31.34; FFU, 12.17; P < 0.0001). There were 6 reaspirations for recurrence (12.7%), and 1 patient underwent unicompartmental knee arthroplasty. There were no infections or other complications. CONCLUSION: Significant clinical improvement in patients with symptomatic popliteal cysts can be achieved via UGAFI as the sole treatment. CLINICAL RELEVANCE: UGAFI is a safe and effective option as the sole treatment modality for symptomatic popliteal cysts.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".