Arthroscopic Graft Reconstruction for Nonrepairable Scapholunate Ligament Injuries
Bibliographic record
Abstract
Disruption of the scapholunate intercarpal ligament (SLIL) remains one of the most challenging wrist pathologies to treat. As this injury progresses, diastasis of the scapholunate interval and secondary carpal malalignment occur, leading to abnormal motion and arthritis. Acute injuries may be repairable, but delayed presentation is common, in which the ligament cannot be fixed primarily. Many reconstructive procedures have been described that improve clinical function, but outcomes are variable and no procedure reliably prevents arthrosis. Some of this variability may be related to disruption of secondary stabilizers during open approaches, which motivated the development of arthroscopic interventions. Arthroscopic SLIL reconstruction is technically demanding but maintains secondary stabilizers and capsular vascularity and allows for treatment after arthroscopic evaluation. We describe an arthroscopic reconstructive technique to re-create the volar and dorsal SLIL while reconstructing the long radiolunate ligament. This approach causes minimal soft-tissue disruption and allows for earlier mobilization compared with open procedures. With arthroscopy being critical to the diagnosis and treatment of SLIL injuries, a reliable arthroscopic reconstructive technique is a vital tool for any surgeon treating this pathology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".