Disparities in access to care at high‐volume institutions for uro‐oncologic procedures
Bibliographic record
Abstract
BACKGROUND: Socioeconomic status represents an established barrier to health care access. Age, sex, and race may also play a role. The authors examined whether these affect the access to high-volume hospitals for uro-oncologic procedures in the United States. METHODS: Within the Nationwide Inpatient Sample (NIS), the authors focused on radical prostatectomy (RP), radical cystectomy, and nephrectomy (Nx) performed within the 5 most contemporary years (2003-2007). Logistic regression models were used to estimate the impact of the primary predictors on the likelihood of receiving care at a high-volume hospital. RESULTS: Between 2003 and 2007, 62,165 RP, 6557 radical cystectomy, and 28,062 Nx cases were recorded within the NIS. Patient age (P = .001), year of surgery (P = .001), Charlson Comorbidity Index (P ≤ .025), median Zip Code income (highest vs lowest quartile, P = .001), and insurance status (private vs Medicare, P = .008) were independent predictors of being treated at high-volume institutions. Moreover, black race was an independent predictor of decreased utilization of high-volume institutions for radical cystectomy (P = .012), and female sex was an independent predictor of decreased utilization of high-volume institutions for Nx (P = .016). CONCLUSIONS: On average, old, sick, poor, and Medicare patients were less likely to be treated at high-volume hospitals for uro-oncologic surgery. Similarly, black patients were less likely to have a radical cystectomy at a high-volume hospital, and female patients were less likely to have an Nx at a high-volume hospital. Selective referral of individuals who are less likely to receive care at such institutions may represent a health care priority intended to optimize outcomes across all population strata.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".