The Prevalence of Nephrolithiasis in Patients with Primary Gout: A Cross-sectional Study Using Helical Computed Tomography
Bibliographic record
Abstract
OBJECTIVE: To investigate the prevalence of nephrolithiasis in gouty patients by computed tomography (CT) imaging and to compare it with the "prevalence" of urolithiasis calculated from histories of urinary tract calculus. METHODS: The kidneys of 383 male patients with primary gout were examined using an unenhanced 2-row helical CT detector, imaging at 2 mm collimation and a helical pitch of 3. The urolithiasis history of the 383 patients was investigated by inquiry. Patients' ages, body mass index, and laboratory data from a 1-hour clearance test were determined. RESULTS: CT scans confirmed nephrolithiasis in 103 (26.9%, 95% confidence interval 22.5%-31.6%) of the 383 gouty patients, and history of urinary calculus was positive in 65 (17.0%, 95% confidence interval 13.4%-21.1%) of the 383. However, 64 (62%) of the 103 stone-formers identified by CT had no history of urolithiasis. There was a significant difference between the ages of the 103 stone-formers identified by CT and the 65 stone-formers identified from the history. CONCLUSION: The prevalence of nephrolithiasis obtained using CT was 26.9% in the 383 patients with primary gout. Our results imply that we cannot determine an accurate prevalence of urolithiasis from a patient's history. Most of the "prevalence" reported in the past may not correspond to a statistically justifiable one, but instead to the "cumulative incidence" during the contraction period of gout. Thus, the prevalence of nephrolithiasis confirmed by a cross-sectional method and the "prevalence" of urolithiasis calculated from patients' calculus histories should be clearly distinguished.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".