Epidemiology of Total Knee Replacement in the United States Medicare Population
Bibliographic record
Abstract
BACKGROUND: There are limited population-based data on the utilization and outcomes of total knee replacement. The aim of the present study was to describe the rates of primary and revision total knee replacement and selected outcomes in persons older than sixty-five years of age in the United States. METHODS: Using Medicare claims, we computed annual incidence rates of unilateral elective primary and revision total knee replacement among United States Medicare beneficiaries in the year 2000. Poisson regression was used to assess the relationships between demographic characteristics and the incidence rates of primary and revision knee replacement. Proportional hazards models were used to examine the relationships between the ninety-day rates of complications and demographic and clinical factors. RESULTS: The rate of primary knee replacement was lower in blacks than in whites and in those qualifying for Medicaid supplementation than in those with higher incomes. The complications observed during the ninety days following primary knee replacement included mortality (0.7%), readmission (0.9%), pulmonary embolus (0.8%), wound infection (0.4%), pneumonia (1.4%), and myocardial infarction (0.8%). The complications observed during the ninety days following revision knee replacement were mortality (1.1%), readmission (4.7%), pulmonary embolus (0.5%), wound infection (1.8%), pneumonia (1.4%), and myocardial infarction (1.0%). Blacks had higher rates of mortality, readmission, and wound infection after primary knee replacement than whites did. Patients who qualified for Medicaid supplementation had higher complication rates, particularly after primary knee replacement. CONCLUSIONS: Overall, the rates of postoperative complications during the ninety days following total knee replacement are low. In the United States, blacks and individuals with low income undergo total knee replacement less frequently and generally have higher rates of adverse outcomes following primary knee replacement.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".