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 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 | 0.005 |
| 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 | 0.001 |
| 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".