Association of race and 30-day postoperative complications after urologic oncology surgery
Bibliographic record
Abstract
INTRODUCTION: We aimed to evaluate the association between race and postoperative complications in patients undergoing urologic cancer surgeries, comparing 30-day outcomes between black and white-identifying patients using propensity score matching. METHODS: Adult patients undergoing urologic cancer surgeries from 2015-2019 were identified from the National Surgical Quality Improvement Program database. Black-identifying patients were matched 1:1 with white-identifying patients based on surgical procedure, demographics, and medical history. The primary outcome was 30-day mortality. Secondary outcomes included specific complications, such as unplanned readmission, reintubation, and reoperation; myocardial infarction; renal insufficiency; cardiac arrest; surgical site infections (SSIs) and septic shock. Odds ratios (ORs) with 95% confidence intervals (CIs) were estimated using logistic regression. RESULTS: Among 110 028 patients (mean age 46.8 years; 79.1% male; 12.7% black-identifying), a matched cohort of 28 056 was analyzed. No significant difference in 30-day mortality (OR 1.18, 95% CI 0.86-1.63, p=0.296) was observed. Secondary outcomes showed higher odds of unplanned readmission (OR 1.12, 95% CI 1.02-1.24, p=0.018), reintubation (OR 1.36, 95% CI 1.03-1.81, p=0.032), renal insufficiency (OR 1.84, 95% CI 1.37-2.47, p<0.001), and cardiac arrest (OR 1.49, 95% CI 1.01-2.20, p=0.043), but lower odds of myocardial infarction (OR 0.65, 95% CI 0.43-0.99, p=0.048), superficial SSIs (OR 0.65, 95% CI 0.50-0.85 p=0.001), and septic shock (OR 0.67, 95% CI 0.45-0.98, p=0.041) among black-identifying patients. CONCLUSIONS: While no significant difference in 30-day mortality was observed, black-identifying patients were at an increased risk of several postoperative complications compared to white-identifying patients. These observations warrant further investigations into health equity within urology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".