Morbidity and mortality of radical prostatectomy differs by insurance status
Bibliographic record
Abstract
BACKGROUND: Private insurance status may favorably affect various health outcomes including those associated with radical prostatectomy (RP). We explored the effect of insurance status on 5 short-term RP outcomes. METHODS: Within the Health Care Utilization Project Nationwide Inpatient Sample (NIS) we focused on RPs performed within the 5 most contemporary years (2003-2007). We tested the rates of blood transfusions, extended length of stay, intraoperative and postoperative complications, as well as in-hospital mortality, stratified according to insurance status. Multivariable logistic regression analyses, fitted with general estimation equations for clustering among hospitals, adjusted for confounding factors. RESULTS: Overall, 61,167 RPs were identified. Of those, private insurance accounted for the majority of cases (n = 41,312, 67.5%), followed by Medicare (n = 18,759, 30.7%) and Medicaid (n = 1096, 1.8%). Insurance status other than private was associated with higher rates of blood transfusions (P < .001), higher overall postoperative complication rates (P < .001), higher rates of hospital stay above the median (P < .001), as well as higher in-hospital mortality (P = .01). In multivariable analyses, compared with patients with private insurance, Medicaid patients had higher rates of blood transfusion (odds ratio [OR] = 1.45, P < .001), length of stay beyond the median (OR = 1.61, P < .001) postoperative complications (OR= 1.24, P = .02), and in-hospital mortality (OR = 4.91, = .01). Similarly, Medicare patients had higher rates of blood transfusions (OR = 1.21, P < .001), overall postoperative complications (OR = 1.17, P×< .001) and length of stay beyond the median (OR = 1.25, P < .001). CONCLUSIONS: Even after adjusting for confounding factors, patients with private insurance have better outcomes than their counterparts with nonprivate insurance.
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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".