A systematic review examining the clinical evidence for the elbow internal joint stabilizer and associated complications
Bibliographic record
Abstract
Purpose: Elbow instability continues to be a challenging entity to manage in orthopedics due to the complex anatomy of the elbow joint. Historically, common management techniques involved ulnohumeral cross pinning, spanning plates or external fixation which can lead to complications such as infection, joint incongruity, or stiffness amongst others. More recently, an internal stabilizer has been introduced to allow early elbow range of motion while stabilizing the elbow. Our review aims to examine the literature pertaining to the internal joint stabilizer (IJS) with a focus on its safety profile and outcomes. Methods: A systematic review of studies published in MEDLINE, EMBASE, Web of Science and Cumulative Index to Nursing and Allied Health literature (CINAHL) was conducted to assess the operative outcomes of the IJS. Data on patient demographics, indications for use, postoperative outcomes, complications, and implant removal were compiled. Results: Nine retrospective studies met the inclusion criteria. 246 patients with a mean age of 47.2 years were included in the analysis of acute and chronic cases of instability. There were 178 cases of acute instability and 64 cases of chronic instability. The mean follow up time was 17.5 months. The average outcome measures were DASH of 23.7 and MEPS of 83.3. Predominant complications included hardware-related complications (18.7 %, n = 46), heterotopic ossification (9.6 %, n = 24), and ulnar or median nerve-related symptoms (6.9 %, n = 17) which led to an average complication rate of 33.7 % and a 18.3 % reoperation rate. The average time to removal was 11.8 weeks with 75 % of patients having implant removal. Conclusion: The IJS shows potential for treating elbow instability but does have limitations like high reoperation rates, hardware complications, and large cost which limit is routine use. Further research is needed to better understand its role.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".