Elbow Arthroplasty Using a Convertible Implant
Bibliographic record
Abstract
Total elbow arthroplasty remains the most definitive functional procedure for patients with end-stage painful arthritis of the elbow. Complication rates have historically been quite high, and early revision was not uncommon. A greater understanding of elbow anatomy and kinematics has led to advances in prosthetic design and surgical technique. The success of modern elbow arthroplasty for low-demand patients with rheumatoid arthritis has approached that of hip and knee arthroplasty. Mechanical failures have been noted to increase as a complication of both longevity and the use of elbow arthroplasty in a younger, higher-demand patient population. As the indications for total elbow arthroplasty widen to include more complex situations, it becomes more important to precisely recreate the flexion-extension axis of the elbow to optimally balance muscle forces and ligaments in an effort to improve implant durability. Advances in implant modularity and instrumentation can make determination and recreation of the flexion-extension axis more reliable and reproducible. An anatomic convertible implant allows the surgeon great versatility in choosing to perform hemiarthroplasty or unlinked or linked total elbow arthroplasty with assurance that later revision can be performed without the compulsory removal of well-fixed components. Conversion from an unlinked to a linked constraint, and visa versa, can be performed at any time. If late conversion is required, it can be performed in a minimally invasive fashion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".