Utility and Practicability of Nephrometry Scoring Systems in Contemporary Clinical Practice—An International Multicentre Perspective
Bibliographic record
Abstract
To conduct a multi-institutional international survey to determine the clinical utility and applicability of nephrometry scoring systems in contemporary clinical practice. Methods: A cross-sectional anonymous 15-item online survey was conducted on REDCap between January 2023 and May 2023. Survey invitations were sent via email within Australia and internationally to urologists who are either members of the Urological Society of Australia and New Zealand (USANZ) or the Urological Association of Asia (UAA) or who have direct professional relationships with their members. The survey underwent a trial run on REDCap with several urologists at our institution to test the technical functionality and comprehension prior to dissemination. Results: First, 158 responses were collected and analysed. Just over half (51%) responded that they use a nephrometry system in clinical practice, and the RENAL nephrometry scoring system is the most commonly used. Amongst respondents who use a nephrometry scoring system, 63% stated that it helps with counselling patients and 54% stated it serves as a decision-making tool on whether to perform a partial or radical nephrectomy. Furthermore, 54% use a nephrometry scoring system in surgical planning meetings, and 67% believe that it is helpful for research purposes. Common concerns included that they are too time-consuming to complete, they are unhelpful for treatment decision-making and they are only useful for research purposes. Conclusions: Nephrometry scoring systems are utilised by roughly one in two urologists in contemporary clinical practice. Further qualitative studies are required to better ascertain perspectives towards them and enhance their clinical applicability.
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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 | 0.038 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".