Disparities in Access to Hospitals with Robotic Surgery for Patients with Prostate Cancer Undergoing Radical Prostatectomy
Bibliographic record
Abstract
PURPOSE: We described population level trends in radical prostatectomy for patients with prostate cancer by hospitals with robotic surgery, and assessed whether socioeconomic disparities exist in access to such hospitals. MATERIALS AND METHODS: After merging the NIS (Nationwide Inpatient Sample) and the AHA (American Hospital Association) survey from 2006 to 2008, we identified 29,837 patients with prostate cancer who underwent radical prostatectomy. The primary outcome was treatment with radical prostatectomy at hospitals that have adopted robotic surgery. Multivariate logistic regression was used to identify patient and hospital characteristics associated with radical prostatectomy performed at hospitals with robotic surgery. RESULTS: Overall 20,424 (68.5%) patients were surgically treated with radical prostatectomy at hospitals with robotic surgery, while 9,413 (31.5%) underwent radical prostatectomy at hospitals without robotic surgery. There was a marked increase in radical prostatectomy at hospital adopters from 55.8% in 2006 and 70.7% in 2007 to 76.1% in 2008 (p <0.001 for trend). After adjusting for patient and hospital features, lower odds of undergoing radical prostatectomy at hospitals with robotic surgery were seen in black patients (OR 0.81, p <0.001) and Hispanic patients (OR 0.77, p <0.001) vs white patients. Compared to having private health insurance, being primarily insured with Medicaid (OR 0.70, p <0.001) was also associated with lower odds of being treated at hospitals with robotic surgery. CONCLUSIONS: Although there was a rapid shift of patients who underwent radical prostatectomy to hospitals with robotic surgery from 2006 to 2008, black and Hispanic patients or those primarily insured by Medicaid were less likely to undergo radical prostatectomy at such hospitals.
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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".