Disparity in Preoperative Patient Factors Between Insurance Types in Total Joint Arthroplasty
Bibliographic record
Abstract
Equity in health care has become a focal point of debate. However, the disparity between insurance payer types in total joint arthroplasty is poorly defined. The authors identified 1312 consecutive patients who underwent elective primary total hip or knee arthroplasty with available preoperative Short Form 36 and Western Ontario and McMaster University Osteoarthritis Index surveys and stratified them into groups based on insurance type (Iowa Care [a state-run insurance program for patients who are indigent], Medicare, Medicaid, or private insurance) to compare demographics, access to care, and functional data. Significance was a P value less than .05 after a Turkey-Kramer adjustment for multiple comparisons. A multivariate analysis identified independent predictors of Short Form 36 and Western Ontario and McMaster University Osteoarthritis Index preoperative functional status. Few differences existed between patients with Iowa Care and Medicaid, but both groups had significantly lower Short Form 36 and Western Ontario and McMaster University Osteoarthritis Index scores across every category compared with patients with Medicare or private insurance (P<.05 for each comparison). In addition, patients with Iowa Care and Medicaid had a higher incidence of current smoking and higher mean body mass index and traveled an average of 29 to 30 miles farther for access to care (P<.05 for each comparison). Payer type was an independent predictor of preoperative Short Form 36 and Western Ontario and McMaster University Osteoarthritis Index functional scores in the multivariate analysis (P<.02). Significant differences exist between payer types in total joint arthroplasty. Further research is necessary to better inform health policy decisions.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".