Incidence, Treatment, and Implications of Kidney Stones During Pregnancy: A Matched Population-Based Cohort Study
Bibliographic record
Abstract
Purpose: To determine the incidence of kidney stones in pregnancy, the risk of adverse birth outcomes, and treatment trends. Methods: We performed a population-based matched cohort study using Ontario's health care databases. All pregnancies in Ontario from 2004 to 2014 were identified. The study exposure was hospital admission, emergency room visit, or intervention for kidney stones during pregnancy. Each pregnancy with a stone was matched to up to six pregnancies without a stone based on age, region of residence, income quintile, year of cohort entry, prior births, and multibirths. The primary outcome was adverse birth outcome defined as preterm birth, low birth weight, or infant death. Secondary outcomes included premature rupture of membranes (PROM), pre-eclampsia, and cesarean section (C/S), as well as the type/frequency of intervention for stones in pregnancy. Logistic regression models, with generalized estimating equations, were used to assess any differences in study outcomes across groups. Results: Of 1.39 million pregnancies identified, there were 2863 pregnancies with stones (0.2%), which were matched with 17,171 pregnancies without stones. Pregnancies with stones had an increased risk for adverse birth outcome compared with matched pregnancies without stones (odds ratio [OR] 1.62, confidence interval [95% CI] 1.43–1.82, p < 0.0001). Pregnancies with stones also had a greater risk for pre-eclampsia (OR 1.42, 95% CI 1.02–1.99, p = 0.04) and C/S (OR 1.39, 95% CI 1.27–1.51, p < 0.0001), but not PROM. Twenty-six percent of pregnant patients admitted for a stone had an intervention, most commonly a stent or ureteroscopy. Conclusion: Our study demonstrated an increased risk of adverse birth outcomes in pregnancies with kidney stones. These results will be important for counseling pregnant patients with kidney stones and women of reproductive age who are at risk of developing stones.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".