Atlas of Scoring Systems, Grading Tools, and Nomograms in Endourology: A Comprehensive Overview from the TOWER Endourological Society Research Group
Bibliographic record
Abstract
Introduction: With an increase in the prevalence of kidney stone disease (KSD), there has been a universal drive to develop reliable and user-friendly tools such as grading systems and predictive nomograms. An atlas of scoring systems (SS), grading tools, and nomograms in Endourology is provided in this article. Methods: A comprehensive search of world literature was performed to identify nomograms, grading systems, and classification tools in endourology related to KSD. Each of these was reviewed by the authors and has been evaluated in a narrative format with details on those that are externally validated and their respective citation count on google scholar. Results: A total of 54 endourological tools have been described in our atlas of endourological SS, grading tools, and nomograms. Of the tools, 23 (43%) have been published in the past 3 years showing an increasing interest in this area. This includes five for percutaneous nephrolithotomy, six for flexible ureteroscopy, three for semi-rigid ureteroscopy (URS), nine for extracorporeal shockwave lithotripsy, two for stent encrustations, three for intraoperative appearance at the time of URS, and three to classify intraoperative ureteric injury. There were three tools for renal colic assessment, one each for prediction of future stone event, stone classification, and stone impaction and two for need of emergency intervention in ureteral stone. Two tools are related to stone recurrence, whereas six are related to postprocedural complications. There are now two tools for simulation in endourology and five for patient-reported outcome measures. Conclusions: A number of reliable and established tools currently exist in endourology. Each of these offers their own respective advantages and disadvantages. Although nomograms and SS can help in the decision making, these must be tailored to individual patients based on their specific clinical scenarios, expectations, and informed consent.
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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 | 0.027 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.054 | 0.041 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".