Do urologists follow the golden rule? A global urolithiasis management study by the Clinical Research Office of the Endourological Society
Bibliographic record
Abstract
INTRODUCTION: The primary objective of this study was to compare surgical management options for various urolithiasis scenarios that urologists would choose for themselves vs. the options they would recommend for their patients. The secondary objective was to identify the common recommended treatments for upper urinary tract stones of various sizes and locations. METHODS: Two surveys were sent by the Clinical Research Office of the Endourological Society (CROES) to members of the Endourological Society. Standard demographic information was collected. The first survey asked the urologists to recommend treatment for urolithiasis in 10 different scenarios assuming that they were the patient with stone disease. The second survey, sent eight months later, asked urologists to recommend treatment for the same 10 scenarios for a theoretical patient. Only urologists who responded to the first and the second survey were included. Recommended treatment options were compared between the surveys. Agreement between the two scenarios was measured with Cohen's kappa. Surveys were conducted on the Internet using SurveyMonkey™. All statistical analyses were performed using R statistical program version 2.12.2. RESULTS: The two surveys had response rates of 78% (160/205) and 84% (172/205), respectively with urologists from 38 countries. Median experience of respondents was seven years (range: 2-30). The majority of respondents, 117 (75%), were affiliated with academic hospitals. Recommended treatments for stone disease in different scenarios were not entirely consistent when the urologists considered themselves as the patients compared to the choice they might recommend for their patients. Cohen's kappa ranged from 0.292-0.534 for the different scenarios. Overall, shock wave lithotripsy (SWL) and ureteroscopy (URS) were the most commonly chosen treatment options, with medical expulsive therapy (MET) and laparoscopy being the least recommended by urologists for themselves, as well as for their patients. CONCLUSIONS: Although urologists were not entirely consistent in their recommendations for stone treatment, they generally followed the "golden rule" and treated their patients as they would want to be treated. The most commonly recommended treatments for upper urinary tract stones were SWL and URS.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".