A prospective evaluation of obesometric parameters associated with renal stone recurrence
Bibliographic record
Abstract
INTRODUCTION: Our aim was to evaluate whether obesometric serum hormones and body fat distribution are associated with renal stone recurrence. METHODS: We conducted a prospective cohort study of participants undergoing renal stone (RS) intervention at a single institution from November 2009-June 2010 and followed them for a median 62 months. Obesometric parameters were measured at baseline, including body mass index (BMI), fasting serum leptin and adiponectin, and proportion of visceral adipose tissue (%VAT) averaged from three fixed axial computed tomography (CT) slices. The primary study outcome was stone recurrence. RESULTS: A total of 110 participants were enrolled. Elevated %VAT was associated RS recurrence; participants with %VAT in the highest quartile had a five-year stone-free rate of 47.1% compared to 72.2% among other participants (p=0.004). Adjusting for gender, elevated %VAT was independently predictive of renal stone recurrence among initial stone formers (n=74; hazard ratio [HR] 4.53, 95% confidence interval [CI] 1.08-19.02), but not among recurrent stone formers (n=19; HR 0.51, 95% CI 0.054-4.72). Other obesometric factors, including leptin, adiponectin, and BMI, were not significantly predictive of recurrence. CONCLUSIONS: We report a novel association between an elevated %VAT and stone recurrence. These findings may inform patient counselling and followup regimens. The metabolic basis for these findings requires further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".