Improvement of racial disparities with respect to the utilization of minimally invasive radical prostatectomy in the United States
Bibliographic record
Abstract
BACKGROUND: Race represents an established barrier to health care access in the United States and elsewhere. We examined whether race affects the utilization rate of minimally invasive radical prostatectomy (MIRP) in a population-based sample of individuals from the United States. METHODS: Within the Healthcare Cost and Utilization Project Nationwide Inpatient Sample (NIS), we focused on patients in whom MIRP and open radical prostatectomy (ORP) were performed between 2001 and 2007. We assessed the proportions and temporal trends in race distributions between MIRP and ORP. Multivariable logistic regression analyses further adjusted for age, year of surgery, baseline Charlson Comorbidity Index, annual hospital caseload tertiles, hospital region, insurance status, and median zip code income. RESULTS: Of 65,148 radical prostatectomies, 3581 (5.5%) were MIRPs. African Americans accounted for 11.4% of patients versus 78.8% for Caucasians versus 9.9% for others. Between 2001 and 2007, the annual proportions of Caucasian patients treated with MIRP were 2.2%, 0.9%, 2.6%, 7.2%, 4.7%, 9.3%, and 11.6%, respectively (chi-square trend p<0.001). For the same years in African American patients, the proportions were 0.8, 0.3, 1.4, 4.4, 3.5, 9.0 and 8.4% (chi-square trend P < .001). In multivariable analyses relative to Caucasian patients, African American patients were 14% less likely to undergo MIRP (P = .01). After period stratification between years 2001-2005 versus 2006-2007, African Americans were 22% less likely to undergo a MIRP in the early period (P = .007) versus 11% less likely to have a MIRP in the contemporary period (P = .1). CONCLUSIONS: The racial discrepancies in MIRP utilization rates are gradually improving.
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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 | 0.002 |
| 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".