Incidence and trends in the treatment of kidney stones in Canada
Bibliographic record
Abstract
INTRODUCTION: Our objective was to assess the incidence of kidney stones requiring acute care, trends in the surgical treatment of stones, and the demographics of stone formers in Canada. METHODS: We conducted a population-based, retrospective cohort study using administrative data from the Canadian Institute for Health Information. We included Canadian residents age >18 years, outside of Quebec, who presented between January 1, 2013, and December 31, 2018, with a kidney stone episode. This was defined as a kidney stone resulting in hospital admission, emergency department visit, or stone intervention, specifically shockwave lithotripsy (SWL), ureteroscopy (URS), or percutaneous nephrolithotomy (PCNL). RESULTS: There were 471 824 kidney stone episodes, including 184 373 interventions. The number of kidney stone episode increased from 277/100 000 in 2013 to 290/100 000 in 2018. The median age was 53 (interquartile range 41-65) years and 59.9% were male. The crude rate for stone intervention was 877/100 000. The age- and gender-standardized rate for interventions was highest in Nova Scotia and Newfoundland and Labrador, and lowest in Prince Edward Island. The most common intervention in Canada was URS (73.5%), followed by SWL (19.8%) and PCNL (6.7%). The percent utilization of SWL was highest in Manitoba, whereas for URS, it was highest in Prince Edward Island and Alberta. CONCLUSIONS: Our study provides the first population-based data on the demographics of stone formers and treatment trends across Canada. There has been a 4.7% increase in kidney stone episodes over the study period. Those presenting to hospital or requiring intervention for a kidney stone are more likely to be male, aged 41-65, and undergo URS.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".