Utility of Ultrasonography and Significance of Surgical Anatomy in the Management of de Quervain Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The role of ultrasound in plastic surgery practice has grown significantly over the past decade, with notable applications for conditions of the upper extremity. Its utility for the management of de Quervain disease, however, remains to be established, and the prevalence of first dorsal compartment anatomical variations needs to be adequately assessed. METHODS: A systematic review was performed to evaluate the role of ultrasound in the diagnosis, anatomical characterization, and clinical management of de Quervain disease. A meta-analysis was conducted to establish the prevalence of first dorsal compartment anatomical variations in the de Quervain disease and general population, along with the diagnostic accuracy of ultrasound for their detection. Outcomes were documented and compared to alternative treatment options. RESULTS: Extensor retinaculum thickening, tendon sheath swelling, peritendinous edema, and tendon enlargement were the most common sonographic features of de Quervain disease. The prevalence of an intercompartmental septum in the de Quervain disease surgical population was shown to be significantly greater than in the general cadaveric population (67 percent versus 35 percent, respectively). Although the efficacy of energy-based therapeutic ultrasound remains elusive, ultrasound-guided corticosteroid injections were shown to be more accurate than manual injections (90 to 100 percent versus 40 to 100 percent), and to confer significantly better treatment outcomes (73 to 100 percent versus 59 to 83 percent success rates, respectively). CONCLUSIONS: Ultrasound use is essential to achieve the best evidence-based outcomes in the management of de Quervain disease. The varied prevalence of first dorsal compartment anatomical variations and high accuracy of ultrasound for their detection carry significant prognostic implications.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.007 | 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) | 0.003 | 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".