The Effect of Obesity on Perioperative Outcomes Following Percutaneous Nephrolithotomy
Bibliographic record
Abstract
OBJECTIVES: To test if obesity predisposes to higher rates of adverse outcomes after percutaneous nephrolithotomy (PCNL). MATERIALS AND METHODS: Within the Nationwide Inpatient Sample (NIS), we identified patients treated with PCNL between 1998 and 2010 for kidney stones. We examined the temporal trends in PCNL use and charges among obese and nonobese patients. We then tested the effect of obesity on perioperative complications, transfusions, length of stay (LOS), and total hospital charges (THCs). LOS and THCs were defined as a continuous variable and were also dichotomized according to the 75th percentile into prolonged LOS (pLOS) and increased THCs (iTHCs). Then, multivariable models were fitted. RESULTS: Overall, a weighted sample of 90,529 individuals treated with PCNL between 1998 and 2010 was examined. Of those patients, 9300 were obese (10.3%). The proportion of PCNLs performed in obese patients increased throughout the years from 7.4% to 16.7% (p < 0.001). Overall complication rates were 21.6% vs 22.0% (p = 0.3) and transfusion rates were 4.3% vs 4.0% (p = 0.1) for obese and nonobese patients, respectively. Obese patients had fewer genitourinary complications (13.4% vs 15.0%, p < 0.001), but had higher rates of sepsis (1.7% vs 1.3%, p = 0.009) as well as respiratory (3.0% vs 2.5%, p = 0.002) and vascular complications (0.3% vs 0.2%, p = 0.007). Conversely, pLOS (20.9% vs 18.8%, p < 0.001) and iTHCs (30.8% vs 24.4%, p < 0.001) were more frequently recorded in obese patients. In multivariable analyses, obesity was neither associated with higher rates of overall complications (odds ratio [OR], p = 0.3) nor with higher rates of transfusions (p = 0.3). However, obesity was associated with pLOS (OR: 1.21, p = 0.002) as well as iTHCs (OR: 1.17, p = 0.002). CONCLUSIONS: PCNL in obese patients did not result in higher rates of individual complications or transfusions. However, it resulted in higher rates of pLOS and iTHCs.
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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 | 0.001 |
| 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 | 0.000 |
| 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".