Stone Burden Measurement by 3D Reconstruction on Noncontrast Computed Tomography Is Not a More Accurate Predictor of Stone-Free Rate After Percutaneous Nephrolithotomy Than 2D Stone Burden Measurements
Bibliographic record
Abstract
Purpose: Stone burden has been reported as an independent predictor of stone-free rate after percutaneous nephrolithotomy (PCNL); however no consensus exists on a standardized method for measuring stone burden. Recently, stone volume has been advocated as the most accurate means of measuring stone burden. We aimed to compare different measuring methods of stone burden and to identify the predictive value of each for outcomes after PCNL. Materials and Methods: We performed a retrospective review of a prospective database of patients who underwent PCNL between 2006 and 2013. A preoperative CT and postoperative imaging at discharge were necessary for eligibility. Stone burden was assessed through four different ways on CT images: (1) cumulative stone diameter; (2) estimated SA (surface area) calculated as longest × orthogonal diameter × π/4; (3) manual outline of stone and computer SA calculation; and (4) automated 3D volume calculation using specific software. Primary outcome was stone-free status (SFS) at discharge. Secondary outcomes included operative time and the need for an ancillary procedure. Regression analysis and receiver operating characteristic curve analysis were used to evaluate the predictive value of each method. Results: Of 313 included patients, 69.6% were stone free at discharge. All measures of stone burden were independent predictors of SFS [OR and 95% CI of 1.027 (1.014, 1.040), 1.481 (1.180, 1.858), 1.736 (1.266, 2.380), and 1.311 (1.127, 1.526), respectively] and demonstrated similar predictive accuracy (area under the curve = 0.630, 0.630, 0.627, and 0.638, respectively). Stone burden by any measure was an independent predictor of operative time and secondary procedure. Conclusions: We demonstrated that measuring stone burden by manual outline or automated 3D volume on reformatted CT images had no added value compared with orthogonal measurement for predicting outcomes after 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".