Geographic Differences in Rates of Primary Total Knee Arthroplasty in Young and Older Adults: A Comparison of 3 US States
Bibliographic record
Abstract
OBJECTIVE: Rates of total knee arthroplasty (TKA) among Medicare beneficiaries (adults aged ≥ 65 yrs) vary across the United States, with higher rates in the Midwest and West than in the South. It is not known if a similar variation is present among younger patients, or if findings in Medicare reflect selective postponement of TKA in some regions. METHODS: Data on all primary TKA performed in adults aged ≥ 20 years in 3 states (Iowa, Utah, and Florida) in 2016 were obtained from state inpatient databases. Rates of TKA were computed based on population census data. Age-, sex-, and race-standardized rates were compared between Iowa and Florida, and between Utah and Florida, among adults aged 20-64 years and adults aged ≥ 65 years. RESULTS: There were 10,074, 8954, and 43,908 primary TKAs in Iowa, Utah, and Florida, respectively. Standardized rates were higher in Iowa and Utah than in Florida among both adults aged 20-64 years (Iowa:Florida rate ratio [RR] 1.89, 95% CI 1.79-1.99; Utah:Florida RR 2.31, 95% CI 2.18-2.45) and those aged ≥ 65 years (Iowa:Florida RR 1.41, 95% CI 1.35-1.47; Utah:Florida RR 1.77, 95% CI 1.70-1.85). Results were similar in sensitivity analyses limited to White patients, urban residents, and those with a diagnosis of knee osteoarthritis. CONCLUSION: TKA rates were higher in Iowa and Utah than in Florida among both younger adults and those aged ≥ 65 years, indicating that geographic differences are not specific to elderly patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".