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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".