Kidney stone disease
Bibliographic record
Abstract
INTRODUCTION: Despite kidney stone disease (KSD) guidelines, high-quality evidence for KSD management in Canada is lacking. We aimed to assess Canadian urologists' practice patterns, preferences, and barriers in managing KSD. METHODS: A cross-sectional survey was distributed to Canadian urologists via the Canadian Urological Association (CUA), Quebec Urological Association (QUA), and Canadian Endourology Group (CEG), as well as directly to urology departments nationwide. Descriptive statistics were used to analyze the results. RESULTS: Of 93 respondents, 47% were from academic centers, 43% from community hospitals, and 10% from mixed/private settings. Most performed over 75 ureteroscopies and fewer than 25 percutaneous nephrolithotomies (PCNLs) annually (67% and 58%, respectively). Holmium:YAG (Ho:YAG) lasers were available in 85% of hospitals, thulium fiber laser (TFL) in 70%, and Ho:YAG with Moses effect lasers in 28%. Preferred surgical devices included the TFL (74.5%), followed by the Ho:YAG laser (24.2%) and Ho:YAG with Moses effect laser (21.7%). Endourology fellowship-trained urologists (53%) were more likely to perform their own PCNL access (90% vs. 23%, p<0.001), metabolic workup (73% vs. 48%, p=0.02), and felt more comfortable prescribing prophylactic and medical treatment for KSD (86% vs. 50%, p<0.01) compared to non-endourology fellowship-trained colleagues. Metabolic workup was delegated to nephrologists or specialized clinics by 38%, mainly due to lack of time (25%) and expertise (25%). Additionally, 71% lacked access to multidisciplinary KSD clinics, with 76% believing such clinics would be beneficial. CONCLUSIONS: The study highlights variability in KSD management practices and barriers. Addressing these issues could improve KSD care in Canada and inform future guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.011 |
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".