Ultrasound-Guided Interventions for Carpal Tunnel Syndrome: A Systematic Review and Meta-Analyses
Bibliographic record
Abstract
Carpal tunnel syndrome (CTS) is the most common peripheral entrapment, and recently, ultrasound-guided perineural injection (UPIT) and percutaneous flexor retinaculum release (UPCTR) have been utilized to treat CTS. However, no systematic review or meta-analysis has included both intervention types of ultrasound-guided interventions for CTS. Therefore, we performed this review using four databases (i.e., PubMed, EMBASE, Scopus, and Cochrane) to evaluate the quality of evidence, effectiveness, and safety of the published studies on ultrasound-guided interventions in CTS. Among sixty studies selected for systemic review, 20 randomized treatment comparison or controlled studies were included in six meta-analyses. Steroid UPIT with ultrasound guidance outperformed that with landmark guidance. UPIT with higher-dose steroids outperformed that with lower-dose steroids. UPIT with 5% dextrose in water (D5W) outperformed control injection and hydrodissection with high-volume D5W was superior to that with low-volume D5W. UPIT with platelet-rich plasma outperformed various control treatments. UPCTR outperformed open surgery in terms of symptom improvement but not functional improvement. No serious adverse events were reported in the studies reviewed. The findings suggest that both UPIT and UPCTR may provide clinically important benefits and appear safe. Further treatment comparison studies are required to determine comparative therapeutic efficacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.021 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".