Kinematics and laxity of a linked total elbow arthroplasty following computer navigated implant positioning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aseptic loosening in total elbow arthroplasty (TEA) remains the most common cause of long-term failure. While several different mechanisms of implant loosening have been suggested, it is likely that one important underlying cause is implant malpositioning, resulting in changes in joint kinematics and loading. Although use of computer navigation has been shown to improve component positioning in other joints, no such system currently exists for the elbow. This study used real-time computer feedback for humeral, ulnar, and radial component positioning in 11 cadaveric extremities. An elbow motion simulator evaluated joint kinematics. Endosteal abutment of the stems of the humeral and ulnar components precluded optimal positioning in 5 and 6 specimens, respectively. Loss of the normal valgus angulation following elbow arthroplasty (p < 0.05) suggests that errors in humeral component positioning translate directly into changes in joint kinematics during active motion. These findings suggest that although computer navigation can reproduce normal joint kinematics, optimal implant positioning may require a TEA system which allows for some modularity to accommodate the normal variations in osseous morphology of the elbow.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 it