Total Elbow Arthroplasty in the Treatment of Posttraumatic Conditions of the Elbow
Bibliographic record
Abstract
Posttraumatic arthritis, arthritis secondary to instability, and nonunion or malunion about the elbow may be treated by various methods. Recommended first-line treatment in the younger, more active patient population is nonprosthetic techniques. Total elbow arthroplasty should be considered primarily as a salvage procedure for these patients. Careful patient selection will determine whether total elbow arthroplasty is an acceptable choice, despite its inherent risks and complications. Prosthetic replacement is more applicable for patients with low physical demands who are older than 60 years of age with pain, stiffness, and/or instability of the elbow who will more likely be able to comply with postoperative rehabilitation and strict activity restrictions. Previous incisions, gross instability, periarticular fibrosis with ulnar nerve encasement, loss of bone and/or soft tissue, and previous infections represent obstacles for prosthetic reconstruction in these patients. The use of unlinked total elbow designs require good bone stock with little deformity and stable capsuloligamentous support, which uncommonly is found in elbows after trauma. Linked semiconstrained prostheses have been used most frequently with good short-term results reported in the literature. Reported failure rates after longer followup have led to a search for improvements in prosthetic design, cementing techniques, and better patient selection.
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 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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.002 |
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".