The impact of the COVID-19 pandemic on kidney stone management in a single-payer system
Bibliographic record
Abstract
INTRODUCTION: We examined the effects of the COVID-19 pandemic on the incidence of kidney stone acute care visits and interventions. METHODS: We conducted a retrospective, population-based cohort study using linked administrative healthcare data in the province of Ontario, Canada. We included all patients who, between March 1, 2018, and September 30, 2021, presented to an emergency department (ED) or were admitted to hospital with renal colic (RC), as well as patients who underwent stenting, nephrostomy tube (NT) insertion, shockwave lithotripsy (SWL), ureteroscopy (URS), or percutaneous nephrolithotomy (PCNL). Using univariate and multivariable analyses, outcomes of interest were compared before and after the onset of COVID-19. RESULTS: Our cohort included 149 006 unique patients; there were 74 994 pre- vs. 94 067 peri-COVID RC episodes (p=0.74). Peri-pandemic patients were more likely to be sicker, female, and from marginalized communities. Mean time from temporizing to definitive intervention increased in the first three months of the pandemic (17.9 vs. 32 days), but no statistically significant effect on the overall proportion of patients undergoing definitive intervention was observed. The onset of COVID-19 was associated with a 29.5% reduction in SWL and a 7.9% and 5.4% increase in URS and NT use, respectively. Hospital admissions for RC increased by 10.9%, while intensive care unit admissions decreased by 25% during the pandemic. CONCLUSIONS: RC incidence and intervention rates were similar before and during the pandemic; however, patient demographics and morbidity differed. Understanding these trends can inform protocols for streamlining care in response to analogous strains on publicly funded healthcare systems.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".