Racial Disparities in 30-day Readmission After Orthopaedic Surgery: A 5-year National Surgical Quality Improvement Program Database Analysis
Bibliographic record
Abstract
BACKGROUND: Readmission rate after surgery is an important outcome measure in revealing disparities. This study aimed to examine how 30-day readmission rates and causes of readmission differ by race and specific injury areas within orthopaedic surgery. METHODS: The American College of Surgeon-National Surgical Quality Improvement Program database was queried for orthopaedic procedures from 2015 to 2019. Patients were stratified by self-reported race. Procedures were stratified using current procedural terminology codes corresponding to given injury areas. Multiple logistic regression was done to evaluate associations between race and all-cause readmission risk, and risk of readmission due to specific causes. RESULTS: Of 780,043 orthopaedic patients, the overall 30-day readmission rate was 4.18%. Black and Asian patients were at greater (OR = 1.18, P < 0.01) and lesser (OR = 0.76, P < 0.01) risk for readmission than White patients, respectively. Black patients were more likely to be readmitted for deep surgical site infection (OR = 1.25, P = 0.03), PE (OR = 1.64, P < 0.01), or wound disruption (OR = 1.45, P < 0.01). For all races, all-cause readmission was highest after spine procedures and lowest after hand/wrist procedures. CONCLUSIONS: Black patients were at greater risk for overall, spine, shoulder/elbow, hand/wrist, and hip/knee all-cause readmission. Asian patients were at lower risk for overall, spine, hand/wrist, and hip/knee surgery all-cause readmission. Our findings can identify complications that should be more carefully monitored in certain patient populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".