Does Education Level Mitigate the Effect of Poverty on Total Knee Arthroplasty Outcomes?
Bibliographic record
Abstract
OBJECTIVE: Total knee arthroplasty (TKA) outcomes are worse for patients from poor neighborhoods, but whether education mitigates the effect of poverty is not known. We assessed the interaction between education and poverty on 2-year Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and function. METHODS: Patient-level variables from an institutional registry were linked to US Census Bureau data (census tract [CT] level). Statistical models including patient and CT-level variables were constructed within multilevel frameworks. Linear mixed-effects models with separate random intercepts for each CT were used to assess the interaction between education and poverty at the individual and community level on WOMAC scores. RESULTS: Of 3,970 TKA patients, 2,438 (61%) had some college or more. Having no college was associated with worse pain and function at baseline and 2 years (P = 0.0001). Living in a poor neighborhood (>20% below poverty line) was associated with worse 2-year pain (P = 0.02) and function (P = 0.006). There was a strong interaction between individual education and community poverty with WOMAC scores at 2 years. Patients without college living in poor communities had pain scores that were ~10 points worse than those with some college (83.4% versus 75.7%; P < 0.0001); in wealthy communities, college was associated with a 1-point difference in pain. Function was similar. CONCLUSION: In poor communities, those without college attain 2-year WOMAC scores that are 10 points worse than those with some college; education has no impact on TKA outcomes in wealthy communities. How education protects those in impoverished communities warrants further study.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".