Association of partial nephrectomy and presence of robotic surgery for kidney cancer in the United States.
Bibliographic record
Abstract
484 Background: While hospital and surgeon characteristics are associated with the type of nephrectomy performed for renal cell carcinoma (RCC), it is unknown whether hospital presence of robotic surgery increases the likelihood of patients receiving partial nephrectomy (PN). Therefore, we evaluate the relationship of PN and hospital presence of robotic surgery from a population-based cohort in the U.S. Methods: After merging the Nationwide Inpatient Sample (NIS) and the American Hospital Association (AHA) survey from 2006 to 2008, we identified 21,999 patients who underwent either PN or radical nephrectomy (RN) for RCC. The primary outcome of this study was the type of nephrectomy performed. Multivariable logistic regression was used to identify hospital characteristics associated with receipt of PN, after adjusting for patient case mix. Results: Overall, we identified 4,832 (22.0%) and 16,347 (88.0%) patients who were surgically treated for RCC with PN and RN, respectively. On multivariable analysis, patients undergoing surgery were more likely to receive PN at academic (OR: 2.77;p<0.001), urban (OR: 3.66; p<0.001), and American College of Surgeon (ACOS) designated cancer centers (OR: 1.10; p<0.05) compared to non-academic, rural, and non-designated hospitals, respectively. After adjusting for patient and hospital characteristics, patients undergoing surgery at hospitals with presence of robotic surgery were also associated with higher adjusted odds ratios for receipt of PN compared to those treated at hospitals without the presence of this advanced treatment technology (OR: 1.28; p<0.001). Conclusions: While academic status and urban locations are established characteristics influencing the type of nephrectomy performed for RCC, ACOS cancer center designation and hospital presence of robotic surgery were also associated with higher use of PN. Our results are informative in identifying key hospital characteristics which may facilitate greater adoption of PN.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".