Limited influence of prosthetic position on aseptic loosening of elbow replacements
Bibliographic record
Abstract
BACKGROUND: Aseptic loosening of elbow replacements, seen in long-term follow-up, remains a problem. In this study, we attempted to determine the influence of cementing technique, prosthetic position, different component sizes, use of a bone plug, and intraoperative fractures on the development and progression of radiolucent lines and aseptic loosening. METHODS: We studied standard radiographs of 125 primary Souter-Strathclyde total elbow prostheses using the Wrightington method. Additionally, 104 preoperative radiographs were available for analysis. We used a Markow statistical model to detect relationships between all factors described above. RESULTS: After a mean follow-up time of 5.5 (2-19) years, 21 (17%) prostheses had loosened radiographically (10-year survival: 65%). When the humeral component was tilted more medially or more anteriorly, we found development of radiolucent lines at the medial condyle and at the posterior side of the humeral component. However, the progression of these lines was not influenced by these positions. No other prognostic factors for radiolucent lines or aseptic loosening were found. INTERPRETATION: Despite the small number of elbows studied, the weak influence of prosthetic position on aseptic loosening gives more ground for a multifactorial cause for aseptic loosening of the Souter-Strathclyde total elbow prosthesis.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".