Trends in revision hip and knee arthroplasty observations after implementation of a regional joint replacement registry
Bibliographic record
Abstract
BACKGROUND: National joint replacement registries outside North America have been effective in reducing revision risk. However, there is little information on the role of smaller regional registries similar to those found in Canada or the United States. We sought to understand trends in total hip (THA) and knee (TKA) arthroplasty revision patterns after implementation of a regional registry. METHODS: We reviewed our regional joint replacement registry containing all 30 252 cases of primary and revision THA and TKA performed between Jan. 1, 2005, and Dec. 31, 2013. Each revision case was stratified into early (< 2 yr), mid (2-10 yr) or late (> 10 yr), and we determined the primary reason for revision. RESULTS: The early revision rate for TKA dropped from 3.0% in 2005 to 1.3% in 2011 (R(2) = 0.84, p = 0.003). Similarly, the early revision rate for THA dropped from 4.2% to 2.1% (R(2) = 0.78, p = 0.008). Despite primary TKA and THA volumes increasing by 35.5% and 39.5%, respectively, there was no concomitant rise in revision volumes. The leading reasons for TKA revision were infection, instability, aseptic loosening and stiffness. The leading reasons for THA revision were infection, instability, aseptic loosening and periprosthetic fracture. There were no discernible trends over time in reasons for early, mid-term or late revision for either TKA or THA. CONCLUSION: After implementation of a regional joint replacement registry we observed a significant reduction in early revision rates. Further work investigating the mechanism by which registry reporting reduces early revision risk is warranted.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".