Preoperative frailty predicts adverse short‐term postoperative outcomes in patients treated with radical nephroureterectomy
Bibliographic record
Abstract
BACKGROUND: To investigate the effect of frailty on short-term postoperative outcomes and total hospital charges (THCs) in patients with non-metastatic upper urinary tract carcinoma, treated with radical nephroureterectomy (RNU). METHODS: Within the National Inpatient Sample (NIS) database we identified 11 258 RNU patients (2000-2015). We used the Johns Hopkins frailty-indicator to stratify patients according to frailty status. Time trends and multivariable logistic, Poisson and linear regression models were applied. RESULTS: Overall, 1801 (16.0%) patients were frail, 4664 (41.4%) were older than 75 years and 1530 (13.6%) had Charlson comorbidity index ≥2. Rates of frail patients increased over time, from 7.3% to 24.9% (P < .001). Frail patients exhibited higher rates (all P < .05) of overall complications (62.6% vs 50.9%), in-hospital mortality (1.6% vs 1.0%), non-home-based discharge (22.7% vs 12.1%), longer length of stay (LOS) (6 vs 1 day) and higher THCs ($49 539 vs $39 644). Moreover, frailty independently predicted (all P < .05) overall complications (OR, 1.46), in-hospital mortality (OR, 1.52), non-home-based discharge (OR, 1.36), longer LOS (RR, 1.30) and higher THCs (RR, +$11 806). CONCLUSION: Preoperative frailty is important in RNU patients. One of four RNU patients is frail. Moreover, frailty predicts short-term postoperative complications, as well as longer LOS and higher THCs after RNU.
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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".