Optimizing Outcomes in Total Elbow Arthroplasty
Bibliographic record
Abstract
The use of total elbow arthroplasty (TEA) is projected to increase by more than 50% between 2020 and 2045. An aging population, contemporary prosthetic designs, and broadened indications are factors associated with this predicted increase. Although TEA can reliably improve pain and function, overall complication rates remain relatively high compared with other arthroplasties, making technical competence of utmost importance. Careful patient selection, preoperative optimization, and thorough counselling on the complication profile and the potential for mechanical failure following TEA are essential. Although debated, surgical exposure to perform TEA should be tailored to the underlying diagnosis and elbow features. Contemporary exposures, including the paraolecranon and the "diamond pop-up," have been popularized only recently. Understanding the nuances of adequate implant positioning, soft-tissue balancing, and good cementation technique can decrease implant interface stresses, impingement, and rotational instability, which have a direct effect on subsequent mechanical failure. The continued success of TEA will depend on advances in surgical planning and technique as well as implant design and materials to improve longevity and allow use with minimal restrictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".