The effect of abdominal fat parameters on percutaneous nephrolithotomy success
Bibliographic record
Abstract
INTRODUCTION: Obesity has been suggested to lower the success of percutaneous nephrolithotomy (PCNL). However, the relationship between abdominal fat parameters, such as visceral and subcutaneous abdominal adipose tissue, and PCNL success remained unclear. In this study, we aimed to investigate the effect of abdominal fat parameters on PCNL success. METHODS: A total of 150 patients who underwent PCNL were retrospectively enrolled in this study. Group 1 consisted of patients who had no residual stones or residual stone fragments <3 mm in diameter while group 2 included patients with residual stone fragments ≥3 mm. PCNL procedure was defined as successful if all stones were eliminated or if there were residual stone fragments <3 mm in diameter confirmed by non-contrast computed tomography (NCCT) performed postoperatively. Preoperative NCCT was used to determine abdominal fat parameters. RESULTS: Group 1 consisted of 117 (78.0%) patients while group 2 included 33 (22.0%) patients. On univariate analysis, stone number, stone surface area (SSA), visceral fat area (VFA), abdominal circumference on computerized tomography (ACCT), and duration of procedure were found to be predictive factors affecting PCNL success. Logistic regression analysis revealed that ACCT and SSA were independent prognostic factors for PCNL success. CONCLUSIONS: PCNL success was not affected by VFA, subcutaneous fat area (SFA) and body mass index (BMI) in our series. However, ACCT and SSA had negative associations with PCNL success. We conclude that both ACCT and SSA can be used as tools for predicting PCNL outcomes.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".