Living in immigrant communities does not impact total knee arthroplasty outcomes: experience from a high-volume center in the United States
Bibliographic record
Abstract
Community characteristics such as poverty affect total knee arthroplasty (TKA) outcomes. However, it is unknown whether other community factors such as immigrant proportion (IP) also affect outcomes. Our objective was to determine the association of neighborhood IP on preoperative (pre-op) and 2-year postoperative (post-op) Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and function after elective TKA. Patients in a high volume institutional TKA registry between May 2007 and February 2011 were retrospectively analyzed. Demographics, pre-op and 2-year post-op WOMAC pain and function scores, and geocodable addresses were obtained. Patient-level variables were linked to US Census Bureau census tract data. The effect of patient and neighborhood-level factors on WOMAC scores were analyzed using linear mixed effects models. 3898 TKA patients were analyzed. Pre-op and 2-year post-op WOMAC pain and function scores were between 2.75–4.88 WOMAC points worse in neighborhoods with a high IP (≥ 40%) compared to low IP (< 10%). In multivariable analyses, these differences were not statistically significant. Women had worse pre-op and 2-year post-op WOMAC scores (all p ≤ 0.04), but this difference was not influenced by neighborhood IP (all p interaction NS). Patients living in high (≥40%) IP neighborhoods do not have worse pre-op or 2-year post-op pain and function outcomes after TKA compared to those living in low (< 10%) IP neighborhoods. Although sex differences favoring males are notable, these differences are not associated with IP. High neighborhood IP do not appear to affect outcomes after 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".