Surgical outcomes of chronic isolated scapholunate interosseous ligament injuries: a systematic review of 805 wrists
Bibliographic record
Abstract
Background: Management of chronic isolated scapholunate interosseous ligament (SLIL) injuries has generated a substantial volume of low-quality literature with descriptions of multiple new surgical techniques, and the impact of instability pattern and the optimal surgical technique remain unclear. The primary goal of this review was to compare clinical, radiographic and patient-rated outcomes between current surgical techniques. Methods: We performed a systematic literature search using multiple databases. We analyzed clinical, radiographic and patient-reported outcomes. We used a fixed-effects model weighted by sample size with combined outcomes estimated via least squares means with 95% confidence intervals. We also performed a subgroup analysis of static versus dynamic instability. Results: We assessed 805 procedures from 37 study groups, with 429 procedures used in subgroup analysis. There were no statistically significant differences in outcomes between surgical techniques or in subgroup analysis. Overall, postoperative wrist flexion and pain scores decreased, and grip strength and patient-rated outcomes improved. Conclusion: Compared to overall preoperative values, modest improvements in pain score, grip strength and functional outcome scores were obtained from a range of reconstructive procedures performed for chronic isolated SLIL injuries. No significant differences could be ascertained between surgical techniques, potentially owing to the low quality of evidence and procedure heterogeneity. This study provides accurate preoperative reference values for future studies, highlights the controversial clinical impact of instability classification, and the need for higher-quality multicentre or collaborative trials to improve our understanding and management of this common injury.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".