Bibliographic record
Abstract
T he authors have prospectively assessed stone recurrence in 110 participants using body mass index (BMI), fasting serum leptin and adiponectin, and proportion of visceral adipose tissue (%VAT) obtained on axial computed tomography (CT) scans. 1 Since BMI does not differentiate between peripheral and central obesity, the authors used elevated %VAT as an indicator for central obesity.While BMI and obesometric serum hormones did not correlate with recurrence, elevated %VAT was independently predictive of urolithiasis recurrence among initial stone formers (hazard ratio [HR] 4.53), but not among recurrent stone formers. 1 This is a very nice prospective study providing evidence for central obesity (elevated %VAT) correlating with stone recurrence.In addition to central obesity, metabolic syndrome traits also include raised triglycerides, reduced highdensity lipoprotein (HDL) cholesterol, raised blood pressure, and raised fasting plasma glucose. 2 Previous studies have shown that patients with three or more metabolic syndrome traits were associated with higher prevalence of urolithiasis. 2Furthermore, patients with metabolic syndrome have been shown to have significantly higher stone recurrence post-percutaneous nephrolithotomy when compared with controls (41.9% vs. 18.9%; p=0.003). 3n addition to the explanations provided by the authors for the lack of correlation of elevated %VAT with stone recurrence in recurrent stone formers, I would like to propose these two hypotheses.Although the authors excluded patients with residual stone burden, it is possible that recurrent stone formers may have had clinically insignificant residual fragments that contributed to stone recurrence rather than stone recurrence being attributed solely to the elevated %VAT and metabolic syndrome.In addition, as the
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".