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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".