Association of type of renal surgery and access to robotic technology for kidney cancer: results from a population‐based cohort
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relationship between partial nephrectomy (PN) and hospital availability of robot-assisted surgery from a population-based cohort in the USA. METHODS: After merging the Nationwide Inpatient Sample (NIS) and the American Hospital Association survey from 2006 to 2008, we identified 21 179 patients who underwent either PN or radical nephrectomy (RN) for renal cell carcinoma (RCC). The primary outcome assessed was the type of nephrectomy performed. Multivariable logistic regression identified the patient and hospital characteristics associated with receipt of PN. RESULTS: We identified 4832 (22.8%) and 16 347 (77.2%) patients who were treated for RCC with PN and RN, respectively. On multivariable analysis, patients were more likely to receive PN at academic centres (odds ratio [OR] 2.77; P < 0.001), urban centres (OR 3.66; P < 0.001) and American College of Surgeons (ACOS)-designated cancer centres (OR: 1.10; P < 0.05) compared with non-academic, rural and non-ACOS-designated cancer centre hospitals, respectively. Robot-assisted surgery availability at a hospital was also associated with a higher adjusted odds of PN compared with centres without that availability (OR 1.28; P < 0.001). CONCLUSIONS: Although academic and urban locations are established factors that affect the receipt of PN for RCC, the availability of robot-assisted surgery at a hospital was also independently associated with higher use of PN. Our results are informative in identifying other 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".