The Impact of Socioeconomic Status on Implant Selection for Patients Undergoing Hip Arthroplasty
Bibliographic record
Abstract
© 2015, UTMJ. All rights reserved. Hip resurfacing arthroplasty (HRA) is an alternative to total hip arthroplasty (THA) that preserves proximal femoral bone stock. Patient socioeconomic status (SES) has been demonstrated to impact access to care for numerous healthcare interventions but little is known about its impact on HRA when compared to THA. The aim of this study was to investigate whether there are disparities in SES for patients receiving HRA or THA. A retrospective database review was conducted comprising 617 hip arthroplasty patients (310 HRA, 307 THA). Patient postal code was used as a surrogate marker for patients’ SES and referenced against Canada Census Tract data to determine patient income. Patients greater than 70 years of age and those who underwent THA as revision or for fractures were excluded from the study. There were 465 patients included in the analysis comprised of 273 HRA and 192 THA patients. HRA patients ($33,240, SD $8,206) had a significantly higher mean income than THA patients ($29,365, SD $7,119, p<0.001). The percentage of patients that underwent HRA compared to THA increased as patients’ SES increased. Patients with an income greater than $25,000 were significantly more likely to undergo HRA rather than THA (OR ≥1.76), compared to patients with an income less than $25,000 in whom THA was more likely. There appears to be a disparity in SES between patients who receive HRA and THA. Further work is needed to better understand the factors that influence the choice of hip replacement for patients requiring surgical intervention.
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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 | 0.007 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".