The epidemiology of revision total knee and hip arthroplasty in England and Wales
Bibliographic record
Abstract
Total knee arthroplasty (TKA) and total hip arthroplasty (THA) are recognised and proven interventions for patients with advanced arthritis. Studies to date have demonstrated a steady increase in the requirement for primary and revision procedures. Projected estimates made for the United States show that by 2030 the demand for primary TKA will grow by 673% and for revision TKA by 601% from the level in 2005. For THA the projected estimates are 174% and 137% for primary and revision surgery, respectively. The purpose of this study was to see if those predictions were similar for England and Wales using data from the National Joint Registry and the Office of National Statistics. Analysis of data for England and Wales suggest that by 2030, the volume of primary and revision TKAs will have increased by 117% and 332%, respectively between 2012 and 2030. The data for the United States translates to a 306% cumulative rate of increase between 2012 and 2030 for revision surgery, which is similar to our predictions for England and Wales. The predictions from the United States for primary TKA were similar to our upper limit projections. For THA, we predicted an increase of 134% and 31% for primary and revision hip surgery, respectively. Our model has limitations, however, it highlights the economic burden of arthroplasty in the future in England and Wales as a real and unaddressed problem. This will have significant implications for the provision of health care and the management of orthopaedic services in the future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".