Comparing different kidney stone scoring systems for predicting percutaneous nephrolithotomy outcomes: A multicenter retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To compare the predictive performance of five previously described scoring systems (i.e., S.T.O.N.E., Guy's, Clinical Research Office of the Endourological Society (CROES), the Seoul National University Renal Stone Complexity (S-RESC) and the new Stone Kidney Size (SKS) score) for postoperative outcomes regarding stone-free rate (SFR) and complications in adult patients. METHODS: Data from 349 patients who underwent percutaneous nephrolithotomy (PCNL) in three urology departments were analyzed. SKS, S.T.O.N.E., S-ReSC, CROES and Guy's nephrolithometry scoring systems were used to retrospectively calculate predictions for each patient. Univariate and multivariate analyses were performed to evaluate factors associated with SFR and complication rates. Receiver operating characteristic (ROC) curves were generated and areas under curves (AUC) were compared to identify the method with the highest predictive value. RESULTS: Median SKS, S.T.O.N.E., S-ReSC, CROES and Guy's scores were 4, 7, 3, 170.8 and 2, respectively. Overall, SFR was 67.0% (234/349) with a complications rate of 36.7% (128/349). AUCs of each method for predicting stone-free status, highlighted reasonable predictive capabilities with 0.709, 0.806, 0 0.869, 0.207, and 0.735, respectively; however, the S-ReSC scoring system had the best discriminative performance. According to multivariate logistic regression and AUC results, none were effectively capable of predicting complications. CONCLUSIONS: All scoring systems correlated significantly with stone-free status; although, S-ReSC appears to have the greatest predictive ability. This method is also relatively easy to implement and highly reproducible. However, none of the methods analyzed are able to accurately predict postoperative complications.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".