Utilization and Short‐Term Outcomes of Primary Total Hip and Knee Arthroplasty in the United States and Canada
Bibliographic record
Abstract
OBJECTIVE: Total knee arthroplasty (TKA) and total hip arthroplasty (THA) are common and effective surgical procedures. This study sought to compare utilization and short-term outcomes of primary TKA and THA in adjacent regions of Canada and the United States. METHODS: The study was designed as a retrospective cohort study of patients who underwent primary TKA or THA, comparing administrative data from New York and Ontario in 2012-2013. Demographic features of the TKA and THA patients, per capita utilization rates, and short-term outcomes were compared between the jurisdictions. RESULTS: A higher percentage of New York hospitals performed TKA compared to Ontario hospitals (75.7% versus 42.1%; P < 0.001), and the mean annual procedural volume for TKAs was lower in New York hospitals (mean 179 versus 327 in Ontario hospitals; P < 0.001). After direct standardization, utilization was significantly lower in New York compared to Ontario, both for TKA (16.1 TKAs versus 21.4 TKAs per 10,000 population per year; P < 0.001) and for THA (10.5 THAs versus 11.5 THAs per 10,000 population per year; P < 0.001). For those who underwent TKA, the length of stay in Ontario hospitals was significantly longer (mean 3.7 days versus 3.4 days in New York hospitals; P < 0.001). A smaller percentage of New York patients were discharged directly home (46.2% versus 90.9% of Ontario patients; P < 0.001), but 30-day and 90-day readmission rates were higher in New York compared to Ontario (30-day rates, 4.6% versus 3.9% [P < 0.001]; 90-day rates, 8.4% versus 6.7% [P < 0.001]). For the THA cohorts, the results with regard to length of stay, discharge disposition, and readmission rates were similar to those for TKA. CONCLUSION: Ontario has higher utilization of total joint arthroplasty than New York but has a smaller percentage of hospitals performing these procedures. Patients are more likely to be discharged home and less likely to be readmitted in Ontario. Our results suggest areas where each jurisdiction could improve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".