Trends in Discharge to Institutional Post‐Acute Care After Total Joint Arthroplasty in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: Recent payment reforms in the United States have been credited with reducing the use of institutional post-acute care (PAC) after total knee arthroplasty (TKA) and total hip arthroplasty (THA). This dual-country study of Canada and the United States compares longitudinal trends in discharge to institutional PAC after primary TKA or THA. METHODS: We conducted serial cross-sectional analyses to compare discharge to institutional PAC trends among adults aged ≥ 66 years undergoing primary TKA or THA in the United States and Canada from 2013 to 2019. Patient-level data were obtained from population-based Medicare claims in the United States and analogous datasets in Ontario. Discharge trends were assessed using standardized differences and linear regression models to evaluate relative changes over time. RESULTS: Patients receiving TKA (2,308,001) and THA (1,234,149) in the United States and Ontario (106,721 and 53,371, respectively) were similar in age (73-74 years) and sex (~60% female). The absolute reduction in institutional PAC discharge over time for TKA was greater in the United States (slope = -3.59) than in Canada (slope = -0.53) (p < 0.0001), but relative reductions (slope = -8.78 in the United States, slope = -6.99 in Canada) were statistically similar (p = 0.08). THA showed a similar trend of absolute reductions; however, the relative reduction trend in the United States (slope = -9.98) was steeper than in Canada (slope = -6.46) (p = 0.0009). CONCLUSIONS: The US payment reforms from 2013 to 2019 were associated with a greater impact on reducing institutional PAC utilization for THA than for TKA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".