Risk of Hospital Encounters With Kidney Stones in Autosomal Dominant Polycystic Kidney Disease: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: There is a perception that patients with autosomal dominant polycystic kidney disease (ADPKD) are more likely to develop kidney stones than the general population. OBJECTIVE: To compare the rate of hospital encounter with kidney stones and the rate of stone interventions between patients with and without ADPKD. DESIGN: Retrospective cohort study. SETTING: Ontario, Canada. PATIENTS: Patients with and without ADPKD who had a prior hospital encounter between 2002 and 2016. MEASUREMENTS: Rate of hospital encounter with kidney stones and rate of stone intervention. METHODS: We used inverse probability exposure weighting based on propensity scores to balance baseline indicators of health between patients with and without ADPKD. We followed each patient until death, emigration, outcomes, or March 31, 2017. We used a Cox proportional hazards model to compare event rates between the two groups. RESULTS: Patients with ADPKD were at higher risk of hospital encounter with stones compared with patients without ADPKD (81 patients of 2094 with ADPKD [3.8%] vs 60 patients of 1902 without ADPKD [3.2%]; 8.9 vs 5.1 events per 1000 person-years; hazard ratio 1.6 [95% CI, 1.3-2.1]). ADPKD was not associated with a higher risk of stone intervention (49 of 2094 [2.3%] vs 47 of 1902 [2.4%]; 5.3 vs 3.9 events per 1000 person-years; hazard ratio 1.2 [95% CI = 0.9-1.3]). LIMITATIONS: We did not have information on kidney stone events outside of the hospital. There is a possibility of residual confounding. CONCLUSION: ADPKD was a significant risk factor for hospital encounters with kidney stones.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".