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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".