Outcomes and complications of distal humeral hemiarthroplasty for distal humeral fractures – A systematic review
Bibliographic record
Abstract
BACKGROUND: Distal humeral hemiarthroplasty has been performed for a variety of indications with the most common being management of distal humeral fractures. This systematic review evaluates the outcomes and complications of distal humeral hemiarthroplasty for this pathology. METHODS: We searched PubMed, EMBASE, and MEDLINE for studies reporting indications and outcomes of patients undergoing distal humeral hemiarthroplasty. Study screening, risk of bias assessment, and data extraction were performed. Summery statistics were provided. RESULTS: = 163) in this review. In all studies, the indication for distal humeral hemiarthroplasty was the presence of an intraarticular, comminuted, unreconstructable fracture. The mean post-operative MEPS, FullDASH, and QuickDASH (SD) scores were 83.6 (6.1) points, 25.4 (10.3), and 15.7 (7.4) points, respectively. The mean post-operative range of motion (SD) was 106° (11°) in the flexion and extension arc and 153° (19°) in the protonation and supination arc. The overall rate of adverse events and complication was 63%. The rate for major complications was 11%. The mean total revision rate was 4% (0% to 15) and total re-operation rate was 29% (0% to 88%). CONCLUSION: Distal humeral hemiarthroplasty is a suitable option for unreconstructable distal humeral fractures and offers good functional outcomes with acceptable complication rates.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| 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) | 0.003 | 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".