The Dorsal Lunate Dislocation: A Systematic Review and Case Series
Bibliographic record
Abstract
Abstract Dorsal lunate dislocation (DLD) is an exceptionally rare injury, typically resulting from high-energy trauma. Due to the scarcity of reported cases, its exact mechanism of injury is not well understood. This study aimed to present three cases of DLD treated at our institution and to perform a systematic review of the literature to evaluate the mechanisms of injury and treatment approaches for pure DLD. A retrospective review was conducted of three patients with pure DLD treated at a single tertiary care center. Data were collected through medical record analysis and radiographic review. Additionally, a systematic literature review was performed to identify reported cases of DLD. Patient demographics, mechanisms of injury, treatment modalities, and clinical outcomes were analyzed. We identified three cases of DLD, while the systematic review uncovered 17 additional cases. Overall, men represented 95% of cases, with high-energy trauma accounting for 85% of injuries. Forced wrist flexion was the most commonly reported mechanism, occurring in 55% of cases. Radiographic analysis showed concomitant wrist fractures in 55% of cases. Treatment strategies included fixation in 65% of patients, ligament repair in 45%, and lunate resection or proximal row carpectomy in 15%. The median follow-up duration was 12 months, with half of the patients achieving favorable outcomes. Notably, two of the three low-energy injury cases were associated with preexisting arthritis. Pure DLD is an extremely rare clinical entity, possibly resulting from axial loading on a flexed wrist or forced hyperflexion. Characteristic radiographic findings include disruption of Gilula's lines and dorsal displacement of the lunate on lateral radiographs. Optimal management involves initial closed reduction followed by surgical fixation and ligamentous repair when indicated to enhance outcomes and restore wrist function.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".