Sonographic Measurement of the Dorsal Capsule After Arthroscopic Dorsal Capsuloligamentous Repair for Scapholunate Complex Injury: A Pilot Study
Bibliographic record
Abstract
Objective: Arthroscopic dorsal capsuloligamentous repair (ADCLR) has emerged as an effective approach for dynamic scapholunate instability. This pilot study was designed to evaluate postoperative capsuloligamentous healing through sonographic assessment and to describe the early clinical outcomes following ADCLR. Materials and Methods: A retrospective analysis was conducted on 15 consecutive patients who underwent ADCLR, between September 2022 and February 2024. Dorsal capsule thickness was measured by sonography preoperatively, at 6 weeks, and at 3 months. Functional outcomes were assessed using the patient-rated wrist evaluation (PRWE) and the quick disabilities of the arm, shoulder, and hand (DASH) questionnaire. Results: The cohort included nine men (60%) and six women (40%) with a mean age of 33.4 ± 12.3 years (range: 16–56). Dorsal capsule thickness significantly increased from baseline (sagittal: 0.27 ± 0.05 cm; transverse: 0.22 ± 0.04 cm) to 6 weeks (sagittal: 0.60 ± 0.09 cm; transverse: 0.47 ± 0.09 cm), before partially regressing at 3 months (sagittal: 0.43 ± 0.10 cm; transverse: 0.39 ± 0.07 cm), suggesting a dynamic healing process. Mean PRWE improved by 27 ± 18.5 points at 3 months and 45.5 ± 9.9 points at 6 months, while Quick DASH scores improved by 12.7 ± 6.5 and 19.7 ± 5.9 points, respectively. Conclusion: Sonographic surveillance after ADCLR revealed transient dorsal capsular thickening, consistent with postoperative healing. These findings would suggest the relevance of sonographic monitoring and warrant future comparative studies, including failed cases, to clarify the correlation between thickening and successful ligamentous healing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".