Prospective evaluation of kidney displacement during supine mini-percutaneous nephrolithotomy: Incidence, significance, and analysis of predictive factors
Bibliographic record
Abstract
INTRODUCTION: Kidney displacement may alter the quality of renal puncture during percutaneous nephrolithotomy (PCNL). The aim of this study was to identify the rate of kidney displacement and parameters associated with kidney displacement in patients who underwent supine mini-PCNL. METHODS: Data of 98 consecutive patients who underwent mini-PCNL was collected prospectively. The patients were grouped as displacement-positive vs. -negative. The parameters collected were age, gender, body mass index, side of the kidney, punctured calyx, fluoroscopy time to successful puncture and tract dilation, stone-free and complication rates, stone diameter, length of the renal artery, and quantity of peri-renal and abdominal fat. Groups were compared for the above listed parameters and logistic regression analysis was performed to identify factors associated with kidney displacement. RESULTS: There were 34 and 64 patients in the displacement-positive and -negative groups, respectively. Groups were similar for stone-free and complication rates. Fluoroscopy time to puncture and tract dilation were longer in the displacement-positive group. Groups were different for renal artery length and peri-renal fat measurements. In multivariate analysis, lower pole puncture, renal artery length, and peri-renal fat measurement were found to be independent predictors of kidney displacement. CONCLUSIONS: Kidney displacement does not alter the success and complication rates, but is associated with longer fluoroscopy times during supine PCNL. In the current study, parameters in preoperative non-contrast computerized tomography (NCCT) associated with kidney displacement were identified. We recommend surgeons evaluate and take into account these parameters during preoperative planning to establish better outcomes and diminish fluoroscopy times.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".