Visceral obesity: A new risk factor for stone disease
Bibliographic record
Abstract
INTRODUCTION: We examined the relationship between stone disease and the amount of visceral adipose tissue measured with unenhanced computed tomography (CT). METHODS: We included 149 patients with complaints of flank pain and kidney stones detected by CT, from August 2012 to April 2013. In addition, as the control group we included 139 healthy individuals, with flank pain within the same time period, with no previous history of urological disease and no current kidney stones identified by CT. Patients were analyzed for age, gender, body mass index, amount of visceral and subcutaneous adipose tissue, and serum level of low-density lipoprotein and triglyceride. RESULTS: There were no differences between groups in terms of gender and age (p = 0.27 and 0.06, respectively). Respective measurements for the stone and control groups for body mass index were 29.1 and 27.6 kg/m(2); for visceral fat measurement 186.0 and 120.2 cm(2); and for subcutaneous fat measurements 275.9 and 261.9 cm(2) (p = 0.01; 0.01 and 0.36, respectively). Using multivariate analysis, the following factors were identified as increasing the risk of kidney stone formation: hyperlipidemia (p = 0.003), hypertension (p = 0.001), and ratio of visceral fat tissue to subcutaneous fat tissue (p = 0.01). Our study has its limitations, including its retrospective nature, its small sample size, possible selection bias, and missing data. The lack of stone composition data is another major limitation of our study. CONCLUSION: The ratio of visceral to subcutaneous adipose tissue, in addition to obesity, hyperlipidemia, and hypertension, was identified as an emerging factor in the formation of kidney stones.
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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 | 0.003 |
| 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 | 0.000 |
| 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".