Only Size Matters in Stone Patients: Computed Tomography Controlled Stone-Free Rates after Mini-Percutaneous Nephrolithotomy
Bibliographic record
Abstract
OBJECTIVE: To examine and predicting stone-free rates (SFRs) after minimally invasive-percutaneous nephrolithotomy (mini-PNL) based on computed tomography (CT), instead of X-ray or ultrasound control. PATIENTS AND METHODS: We identified 146 mini-PNL patients with pre- and postoperative CT scans. Patient and stone characteristics were assessed. Stone-free status was defined as ≤3 mm residual fragment after mini-PNL according to postsurgery CT scan. Multivariable logistic regression analyses predicted stone-free status after mini-PNL. RESULTS: Overall, 62 (42.5%) patients achieved stone-free status after mini-PNL. In multivariable analyses, stone size was the only independent predictor for stone-free status (OR 0.9; p = 0.02). Patients with stones > 20 mm were less likely to achieve stone-free status, than those harboring stones 10-20 mm (OR 0.3; p = 0.009). SFRs according to stone size categories (< 10, 10-20, and > 20 mm) were 33.3, 50.5, and 25%. Body mass index (BMI) and stone density (Houndsfield units) were no independent predictors for stone-free status after mini-PNL. CONCLUSIONS: We report lower SFRs than expected. Stone size was the only independent predictor for stone-free status after mini-PNL. Patients with larger stones need to be informed about high risk of additional interventions. High BMI and high stone density do not represent a barrier for stone-free status after mini-PNL.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".