Multivariate analysis of risk factors for re-dislocation after revision for dislocation after total hip arthroplasty
Bibliographic record
Abstract
BACKGROUND: The treatment for recurrent dislocation of a total hip arthroplasty is surgical using varied techniques and technologies to reduce the chances of re-dislocation and re-revision. The goal of this study is to compare operative techniques to reduce re-dislocation and re-revision in revision hip arthroplasty due to recurrent dislocations. METHODS: A retrospective study of revision hip arthroplasties done due to recurrent dislocation prior to 01 January 2014. Electronic physician and provincial health records were used to collect patients' initial and follow-up data. Treatment failure was defined as either aseptic re-revision or re-dislocation without revision. Time to event was considered as the re-revision date or the date of second dislocation when the latter endpoint was used. RESULTS: Of 379 operations, 88 (23.2%) had aseptic repeat revision or recurrent dislocation. Of these: 66 (75.0%) due to dislocation with re-revision; 10 (11.4%) due to dislocation with no re-revision surgery; 5 (5.7%) due to aseptic loosening of components; 3 (3.4%) due to osteolysis; 3 (3.4%) due to pseudotumour; and 1 (1.1%) due to periprosthetic fracture. The following factors increase risk of failure: the use of augmented-liners (lipped, oblique and high-offset liners; HR = 1.68, 95% CI, 1.05-2.69), periprosthetic femur fracture (HR = 2.80, 95% CI, 1.39-8.21) and pelvic discontinuity (HR = 3.69, 95% CI, 1.66-8.21). Femur head sizes 36-40 mm are protective (HR = 0.54, 95% CI, 0.31-0.86). In abductor dysfunction the use of focal constrained liners decreases the risk of failure (HR = 0.13, 95% CI, 0.018-0.973). CONCLUSIONS: Larger head sizes and focal constrained liners (abductors dysfunction) should be used and fully constrained liners and augmented-liners should be avoided in a revision hip arthroplasty due to recurrent dislocations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".