Ligament Sparing Elbow Hemiarthroplasty: A Novel Technique for the Management of Distal Humeral Fractures
Bibliographic record
Abstract
Intra-articular distal humerus fractures present various challenges with a wide array of treatment options. Open reduction internal fixation remains the treatment of choice. In older patient populations with poor bone quality and short-end segment fractures with articular comminution, open reduction internal fixation, however, may bring on unsurmountable technical challenges. Total elbow arthroplasty and elbow hemiarthroplasty (EHA) may offer superior functional outcomes in these cases. During EHA for fractures, the medial and lateral columns are reconstructed with the collateral ligaments to restore elbow stability. We hypothesize that in coronal sheer fracture patterns where the columns are intact, maintaining the native collateral ligaments and columns will provide both an anatomic and stable elbow joint. We introduce the ligament sparing EHA technique for unreconstructible coronal shear fractures. We describe this novel technique and compare our postoperative outcomes in 2 patients who underwent this surgery to those described in the literature. The postoperative Disabilities of the Arm, Shoulder, and Hand scores for the 2 patients were 13.8 and 10.3, respectively. The Mayo Elbow Performance Score for the 2 patients were 80 and 85, respectively. The operative arm presented a grip strength of 82% and 89% when compared with the contralateral arm, for the patients respectively. The range of motion varied between 78% and 100% of the contralateral arm for both patients. Although our results are promising and the ligament sparing EHA technique may be a more anatomic option in certain fracture patterns, further research with larger cohorts and multiple surgeons is needed to reinforce our results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".