Robotic vs. open partial cytoreductive nephrectomy in metastatic renal cell carcinoma: adverse in-hospital outcomes
Bibliographic record
Abstract
OBJECTIVE: To test for adverse in-hospital outcomes after robotic (RPCN) vs. open partial cytoreductive nephrectomy (OPCN). METHODS: RPCN and OPCN patients were retrospectively identified within the National Inpatient Sample database (2008-2019). Propensity score matching (PSM, ratio 1:2) and multivariable logistic regression models (LRM) were used. RESULTS: Of 491 patients, 139 (28%) underwent RPCN vs. 352 (72%) OPCN. RPCN-rate increased from 4.2 to 42.5% over time (p < 0.001). RPCN patients exhibited similar age, comorbidity and race/ethnicity distribution relative to their OPCN counterparts. After 1:2 PSM, all 139 RPCN and 278 of 352 (79%) OPCN patients were included. Relative to OPCN, RPCN patients exhibited lower rates in four of 10 examined adverse in-hospital outcomes: intraoperative complications (< 3 vs. 9%, p = 0.02), pulmonary complications (6 vs. 14%, p = 0.02), blood transfusions (< 5 vs. 14%, p = 0.004) and exhibited shorter median length of stay (2 vs. 4 days, p < 0.001). In multivariable LRMs, RPCN independently predicted lower rates in the same four of 10 categories with odds ratio (OR) ranging from 0.17 to 0.34. Largest magnitude was recorded in shorter length of stay (OR 0.17, p < 0.001), followed by intraoperative complications (OR 0.24, p = 0.02), use of blood transfusions (OR 0.25, p = 0.003) and pulmonary complications (OR 0.34, p = 0.01). No differences in in-hospital mortality were recorded. CONCLUSION: Rates of RPCN has increased exponentially over time (4.2 to 42.5%). Relative to OPCN, RPCN is associated with fewer adverse in-hospital outcomes and shorter hospital stay. However, no differences regarding in-hospital mortality were recorded between RPCN and OPCN.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".