Critical Care Therapy After Cytoreductive Nephrectomy for Metastatic Kidney Cancer
Bibliographic record
Abstract
OBJECTIVES: To examine critical care therapy rates after cytoreductive nephrectomy in metastatic kidney cancer patients. DESIGN, SETTING, AND PATIENTS: Relying on the National Inpatient Sample (2000-2019), we addressed critical care therapy use (total parenteral nutrition, invasive mechanical ventilation, renal replacement therapy, percutaneous endoscopic gastrostomy tube insertion, and tracheostomy) and in-hospital mortality in surgically treated metastatic kidney cancer patients. Estimated annual percentage changes and multivariable logistic regression models were fitted. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 10,915 patients, 802 (7.3%) received critical care therapy and 249 (2.4%) died in-hospital. Over time, critical care therapy rates did not differ significantly (6.6% in 2000 to 5.7% in 2019; p = 0.07), while in-hospital mortality decreased from 2.3% to 1.9% (p = 0.004). Age 71 years old or older (odds ratio [OR], 1.43; p < 0.001) and higher comorbidity burden (Charlson Comorbidity Index [CCI] ≥ 3: OR, 2.92; p < 0.001 and CCI 1-2: OR, 1.45; p < 0.001) independently predicted higher critical care therapy rates. Conversely, partial nephrectomy (OR, 0.51; p = 0.003) and minimally invasive surgery (OR, 0.33; p < 0.001) predicted lower critical care therapy rates. Virtually the same associations were recorded for in-hospital mortality. CONCLUSIONS: After cytoreductive nephrectomy, critical care therapy rate was 7.3% vs. in-hospital mortality was 2.4%. Of patients at highest risk of critical care therapy need were those with CCI greater than or equal to 3 and those 71 years old or older. Ideally, these patients should represent targets for thorough assessment of risk factors for complications before cytoreductive nephrectomy.
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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 | 0.001 | 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) | 0.001 | 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".