In focus: perplexing increase of urinary stone disease in children, adolescent and young adult women and its economic impact
Bibliographic record
Abstract
Background: Urinary stone disease (USD) historically has affected older men, but studies suggest recent increases in women, leading to a near identical sex incidence ratio. USD incidence has doubled every 10 years, with disproportionate increases amongst children, adolescent, and young adult (AYA) women. USD stone composition in women is frequently apatite (calcium phosphate), which forms in a higher urine pH, low urinary citrate, and an abundance of urinary uric acid, while men produce more calcium oxalate stones. The reasons for this epidemiological trend are unknown. Methods: This perspective presents the extent of USD with data from a Canadian Province and a North American institution, explanations for these findings and offers potential solutions to decrease this trend. We describe the economic impact of USD. Findings: There was a significant increase of 46% in overall surgical interventions for USD in Ontario. The incidence rose from 47.0/100,000 in 2002 to 68.7/100,000 population in 2016. In a single United States institution, the overall USD annual unique patient count rose from 10,612 to 17,706 from 2015 to 2019, and the proportion of women with USD was much higher than expected. In the 10-17-year-old patients, 50.1% were girls; with 57.5% in the 18-34 age group and 53.6% in the 35-44 age group. The roles of obesity, diet, hormones, environmental factors, infections, and antibiotics, as well as the economic impact, are discussed. Interpretation: We confirm the significant increase in USD among women. We offer potential explanations for this sex disparity, including microbiological and pathophysiological aspects. We also outline innovative solutions - that may require steps beyond typical preventive and treatment recommendations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".