In-Hospital Outcomes after Robotic <i>Vs</i> Open Radical Nephroureterectomy
Bibliographic record
Abstract
Objective: To test whether the contemporary robot-assisted nephroureterectomy (RNU) is associated with more favorable in-hospital outcomes than historical RNU, relative to the same endpoints in open NU (ONU). Methods: Within the National Inpatient Sample (2008–2019), we identified RNU and ONU patients. Multivariable logistic and Poisson regression models were fitted. Results: Of 8032 NU patients, historical (2008–2013) vs contemporary (2014–2019) proportions were 776 (41%) vs 1104 (59%) for RNU and 3719 (60%) vs 2433 (40%) for ONU. The rates of RNU have increased over time (2008–2019; Δ absolute: +18%; p < 0.001). Contemporary RNU patients exhibited significantly better in-hospital outcomes in 6 of 12 comparisons vs historical that ranged from −54% for genitourinary complications to −12% for median length of stay (LOS). Contemporary ONU patients also exhibited significantly better in-hospital outcomes in 11 of 12 comparisons vs historical that ranged from −67% for blood transfusions to −26% for gastrointestinal complications. When historical RNU was compared with historical ONU, RNU in-hospital outcomes were better in 7 of 12 comparisons that ranged from −61% for median LOS to −16% for postoperative complications. Conversely, when contemporary RNU was compared with contemporary ONU, RNU in-hospital outcomes were only better in 2 of 12 comparisons: −25% cardiac complications and −13% for median LOS. Conclusion: The magnitude of in-hospital outcomes categories improvement between historical vs contemporary was two-fold more pronounced in ONU (11 improved categories) than in RNU (6 improved categories). Few outcome benefits remained (two categories only) when contemporary RNU was compared with contemporary ONU.
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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) | 0.001 | 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 | 0.001 |
| 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".