Dislocation after the first and multiple revision total hip arthroplasty: comparison between acetabulum-only, femur-only and both component revision hip arthroplasty
Bibliographic record
Abstract
BACKGROUND: Dislocation may complicate revision total hip arthroplasty (THA). We examined the correlation between the components revised during hip arthroplasty (femur only, acetabulum only and both components) to the rates of dislocation in the first and multiple revision THA. METHODS: We obtained data from consecutive revision THAs performed between January 1982 and December 2005. Patients were grouped into femur-only revision, acetabulum-only revision and revision THA for both components. RESULTS: A total of 749 revision THAs performed during the study period met our inclusion criteria: 369 first-time revisions and 380 repeated revisions. Dislocation rates in patients undergoing first-time revisions (5.69%) were significantly lower than in those undergoing repeated revisions (10.47%; p = 0.022). Within the group of first-time revisions, dislocation rates for acetabulum-only revisions (10.28%) were significantly higher than those for both components (4.61%) and femur-only (0%) reconstructions (p = 0.025). CONCLUSION: Although patients undergoing first-time revisions had lower rates of dislocations than those undergoing repeated revisions, acetabulum-only reconstructions performed at first-time revision arthroplasty entailed an increased risk for instability.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".