Multicenter External Validation and Comparison of Stone Scoring Systems in Predicting Outcomes After Percutaneous Nephrolithotomy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Several scoring systems have recently emerged to predict stone-free rate (SFR) and complications after percutaneous nephrolithotomy (PCNL). We aimed to compare the most commonly used scoring systems (Guy's stone score, S.T.O.N.E. nephrolithometry, and CROES nomogram), assess their predictive accuracy for SFR and other postoperative variables, and develop a risk group stratification based on these scoring systems. MATERIALS AND METHODS: We performed a retrospective review of patients who have had a PCNL at four academic institutions between 2006 and 2013. Primary outcome was SFR within 3 weeks of the surgery and secondary outcomes were operative time (OT), complications, and length of stay (LOS). We performed chi-squared, t-test, logistic, linear, and Poisson regressions, as well as receiver operating characteristics curve with area under the curve (AUC) calculation. RESULTS: We identified 586 patients eligible for analysis. Of these, 67.4% were stone free. Guy's, S.T.O.N.E., and CROES score were predictive of SFR on multivariable logistic regression (odds ratio [OR]: 1.398, 95% confidence interval [CI]: 1.056, 1.852, p = 0.019; OR: 1.417, 85% CI: 1.231, 1.631, p < 0.001; OR: 0.993, 95% CI: 0.988, 0.998, p = 0.004) and have similar predictive accuracy with AUCs of 0.629, 0.671, and 0.646, respectively. On multivariable linear regression, only S.T.O.N.E. was an independent predictor of longer OT (β = 14.556, 95% CI: 12.453, 16.660, p < 0.001). None of the scores were independent predictors of postoperative complications or a longer LOS. Poisson regression allowed for risk group stratification and showed the S.T.O.N.E. score and CROES nomogram to have the most distinct risk groups. CONCLUSIONS: The three evaluated scoring systems have similar predictive accuracy of SFR. S.T.O.N.E. has additional value in predicting OT. Risk group stratification can be used for patient counseling. Further research is needed to identify whether or not any is superior to the others with regard to clinical usefulness and predictive accuracy.
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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 | 0.043 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".