A population-based study on the association between urinary calculi and kidney cancer
Bibliographic record
Abstract
BACKGROUND: Using a nationwide population-based dataset and case-control study design, we investigate the association between urinary calculi (UC) and kidney cancer (KC) in Taiwan. METHODS: The data for this case-control study were sourced from the Taiwan National Health Insurance program. The cases included 1308 incident patients pathologically diagnosed with KC. This study also used 6540 randomly selected subjects as controls. Conditional logistic regression was used to examine the associations between KC and patients previously diagnosed with UC. RESULTS: Of the sampled patients, 1262 (16.1 %) had previously been diagnosed with UC; 415 (31.7 % of the patients with KC) and 847 controls (13.0 % of patients without KC). After adjusting for monthly income, geographic location, urbanization level, hypertension, diabetes, renal disease, obesity, cystic kidney disease, tobacco use disorder, and alcohol abuse, we found that patients with KC were likely to have been previously diagnosed with UC than controls (odds ratio [OR] 3.18, 95% confidence interval [CI] 2.75-3.68, p < 0.001). In addition, the magnitude of the observed associations were stronger among females (females OR 3.59; 95% CI 2.87-4.48 vs. males OR 2.93, 95% CI 2.42-3.55) and transitional cell carcinoma patients (transitional cell carcinoma, OR 3.96; 95% CI 3.23-4.86 vs. renal cell carcinoma OR 2.76, 95% CI 2.31-3.29). CONCLUSIONS: We conclude that there is an association between KC and prior UC, especially in females and patients with transitional cell carcinoma.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".