Patient Outcomes Following Reduction and Association of the Scaphoid and Lunate: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Scapholunate interosseous ligament (SLIL) injury is a common ligamentous injury of the wrist; however, the optimal operative management strategy remains unclear. The objective of this study was to investigate patient outcomes following the Reduction and Association of the Scaphoid and Lunate (RASL) procedure. Materials and Methods: Twenty-five consecutive patients who had an SLIL tear treated with RASL completed a demographic survey and three standardized patient-reported outcome tools (Disabilities of the Shoulder, Arm and Hand [DASH], Patient-Rated Wrist Evaluation [PRWE], and Patient Reported Outcome Measurement Information System, Upper Extremity [PROMIS] questionnaires). Standard wrist radiographs were taken preoperatively and postoperatively and bilateral wrist range of motion was measured. Results: At an average postsurgical time of 4.6 years, the average DASH score was 10.5 with a right-skewed distribution. There was no correlation between screw angle, preoperative scapholunate angle, or time from surgery and DASH score. Conclusion: We conclude that with meticulous surgical technique, patient reported and radiographic outcomes demonstrate the relative success of the RASL procedure as a viable option for SLIL reconstruction in appropriate candidates. Level of Evidence: Level IV evidence-a retrospective cohort study.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".