MARGINALIZATION INFLUENCES ACCESS, OUTCOMES, AND DISCHARGE DESTINATION FOLLOWING TOTAL JOINT REPLACEMENT IN CANADA'S UNIVERSAL HEALTHCARE SYSTEM
Bibliographic record
Abstract
The influence of socioeconomic status (SES) or marginalization on outcomes and complications from total joint arthroplasty (TJA) in the context of the Canadian single-payer healthcare system has yet to be elucidated. The objectives of the current study are to evaluate the impact of SES on TJA surgery outcomes and complications. A population-based cohort study was conducted using a combination of the prospectively collected hospital-based TJA database, and the Canadian Institute of Health Information (CIHI) Discharge Abstract Database. The primary independent variable was the average census marginalization index to infer the patient's SES. The primary dependent variable was functional outcome scores collected at 1-year postoperatively utilizing the Oxford Hip and Knee Scores (OKS/OHS). Multivariate linear regressions were performed to identify independent predictors associated with minimal important difference (MID) of 1-year postoperative OKS/OHS compared to pre-operative, and discharge to an inpatient facility. A consecutive series of 7,286 TJA events were included for analysis. Demographic and outcome characteristics differed significantly across marginalization quintiles. When compared to the highest quintile, quintiles IV and V had significantly lower preoperative and postoperative OKS/OHS scores. When compared to the first quintile, patients in quintile V were significantly older (67.1±9.8 vs. 69.3±10.4, Mean Difference [MD] = 2.2, p 0.5). Patients who are most marginalized received their TJA later in life, had worse preoperative function, experienced worse postoperative function and were more likely to be discharged to another inpatient facility. Identifying patient populations that may benefit from dedicated care pathways will be a valuable tool to increase access to care, improve patient outcomes and reduce health care expenditures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".