Predictors of Fluoroscopy Time During Percutaneous Nephrolithotomy: Impact of Postgraduate Urology Trainees and S.T.O.N.E. Nephrolithometry Score
Bibliographic record
Abstract
OBJECTIVES: To determine predictors of fluoroscopy time (FT) during percutaneous nephrolithotomy (PCNL) and assess the impact of urology postgraduate trainees (PGTs) and S.T.O.N.E. nephrolithometry score. METHODS: A retrospective review of patients undergoing PCNL between 2010 and 2013 at a tertiary healthcare center was performed. Patients' demographics, stone characteristics, including S.T.O.N.E. nephrolithometry score, and operative data were compared among PGTs. Predictors of FT were determined using univariate and multivariate models. RESULTS: A total of 103 PCNLs were assisted by 10 PGTs from postgraduate years (PGYs) 4 and 5 (37 [35.9%] and 66 [64.1%] cases, respectively). Sixty percent of patients were males with a mean age of 55.2±1.5 years and a mean body mass index of 26.4±0.5 kg/m(2). The mean S.T.O.N.E. score was 7.7±0.1, with tubeless PCNL in 53 (51.5%) cases. The mean FT was 120±5 seconds, mean operative time was 102±3.5 minutes, and mean length of hospital stay was 4.2±0.34 days. The overall stone-free rate was 72.8%. PGY-5 trainees used significantly less FT than PGY-4 trainees (115±6 seconds vs 130±7 seconds; p=0.04). FT significantly correlated with the number of involved calices (r=0.24; p=0.02), number of punctures (r=0.6; p=0.01), number of tracts (r=0.4; p=0.01), and operative time (r=0.4; p=0.01). In addition, cases with estimated blood loss (EBL) <250 mL were associated with significantly less FT than those with blood loss >250 mL (109±5.1 seconds vs 148.2±10.9 seconds; p=0.001). On multivariate analysis, the number of punctures, EBL, and operative time were found to be independent predictors for FT. However, there was no correlation of FT with the S.T.O.N.E. nephrolithometry score (r=0.16; p=0.1). CONCLUSIONS: The number of punctures, EBL, and operative time were the only independent predictors of prolonged FT during PCNL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".