Front-to-Back Arthroscopic Repair of Complete Lunotriquetral Ligament Injuries: A Case Presentation and Algorithm for Arthroscopic Management of Intercarpal Ligament Injuries
Bibliographic record
Abstract
The lunotriquetral intercarpal ligament (LTIL) is an important structure that equalizes the forces on the lunate imparted through the scapholunate intercarpal ligament. The extension moment of the triquetrum balances the flexion force of the scaphoid, positioning the lunate for efficient load transfer from the hand to the wrist. In contrast to the scapholunate intercarpal ligament, the LTIL is strongest volarly, with the most critical region being associated with the volar ulnocapitate ligament. Injury to the LTIL is less well understood in comparison to the scapholunate intercarpal ligament but is thought to arise from a fall on a radially deviated, flexed, and pronated wrist, or through attenuation related to ulnar impaction. The uncommon nature of this pathology has resulted in sparse literature, but it should always be considered in ulnar sided wrist pain. Arthroscopy is the gold standard tool for diagnosis, but treatments have been limited to thermal shrinkage or debridement, with reparative interventions classically being performed using an open approach. With advances in arthroscopic techniques, repairs and reconstructions are becoming possible and confer advantages such as less soft tissue stripping and accurate intra-articular joint evaluation. We demonstrate an arthroscopic repair of the volar and dorsal LTIL and propose a treatment methodology to incorporate arthroscopy into the treatment of these injuries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".