Trends in surgery for upper urinary tract calculi in the <scp>USA</scp> using the <scp>N</scp> ationwide <scp>I</scp> npatient <scp>S</scp> ample: 1999–2009
Bibliographic record
Abstract
OBJECTIVE: To determine trends in demographics and treatment for inpatient upper urinary tract calculi in the USA using a population-based cohort. PATIENTS AND METHODS: All patients with a primary or secondary diagnosis of kidney or ureteric calculus between 1999 and 2009 in the US Nationwide Inpatient Sample were extracted and weighted. Temporal trend analyses were used to determine trends in gender, race and age presentation, as well as utilization rates of interventions. Temporal trends were quantified using the estimated annual percent change (EAPC) using least squares linear regression analysis. RESULTS: Overall, 2 109 455 patients were hospitalized with upper urinary tract calculi over the 11-year period. The majority of admissions were for ureteric calculi (63.4%). Admissions for renal calculus increased by 12.1% during the study period (EAPC + 0.92%, P = 0.039, 95% CI: 0.17-1.66), whilst discharges for ureteric calculus remained stable. A significant increase (25.4%) in hospitalizations for women was found (EAPC + 2.21%, P < 0.001, 95% CI: 1.40-3.03); by 2006, more women than men were admitted to hospital (95 953 vs. 94 556, respectively). There were significant increases in hospitalization for black, Hispanic and older patients. Significant changes in the use of all studied interventions were found except for ureteroscopy, extracorporeal shockwave lithotripsy and nephrectomy. CONCLUSIONS: In this nationally representative sample of inpatient discharges, significant increases were found in admissions for renal compared with ureteric calculi, and for black, Hispanic and older patients. With regard to surgical intervention, the largest increase was found in the use of procedures for kidney calculi. Women now comprise the majority in the inpatient management of stone disease.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".