Systematic Review of the Prevalence of Kidney Stones in Cystic Fibrosis
Bibliographic record
Abstract
Purpose: To investigate the prevalence of urolithiasis in cystic fibrosis (CF) and to summarize the available clinical features within this unique population. Methods: Studies reporting the prevalence of urolithiasis in CF patients were identified by a systematic search of the literature from inception to July 31, 2020 on three databases: Ovid Medline, Ovid Embase, and Web of Science. Data were extracted on a predetermined standardized form by two independent authors. Results: A total of 596 publications were retrieved and screened, 15 of which met the eligibility criteria. The publications were published between 1993 and 2019 and were all observational in design. There was a total of 2982 patients with CF included in this review. The overall prevalence of stone formation in the CF population was 4.6% (137/2982). The mean age of diagnosis was 25.1 ± 9.6 and ranged from 0.25 to 47. Ultrasound was the most common imaging modality for kidney stone diagnosis. There was no apparent sex difference, with a female to male ratio of 1:1. Surgical intervention was required in 37.8% (34/90) of cases. Stone recurrence was reported in 42.9% (33/77) of stone formers. Conclusions: This review provides the most recent update for the prevalence of urolithiasis in CF patients and summarizes the available clinical data. Our findings suggest that patients with CF could be at risk for developing stones at a younger age and require interventional management strategies at higher rates compared with the general population. Given the heterogeneity of the literature for urolithiasis in CF, larger population-based studies reporting the epidemiology, clinical features, and management strategies are required to further our understanding of urolithiasis in CF.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".