Identifying risk factors for development of nephrolithiasis in end-stage renal disease patients
Bibliographic record
Abstract
INTRODUCTION: We sought to assess the incidence and risk factors for stone development in patients with end-stage renal disease (ESRD) on hemodialysis (HD). METHODS: Medical records of patients receiving HD between 2007 and 2017 were retrospectively reviewed. Patients who had been on HD for at least three months and had imaging studies (computed tomography [CT] scans or ultrasound [US]) pre- and post-initiation of HD were included. Exclusion criterion was presence of stones pre-HD. De novo stones were defined as renal stones found on followup imaging. Demographics, laboratory data, comorbidities, and dialysis characteristics were compared between non-stone-formers and stone-formers using propensity score matching. RESULTS: , and median dialysis duration 59.5 months. After HD, 14 (10.5%) patients developed de novo stones and their median dialysis-to-stone duration was 23.5 months. When compared with non-stone-formers, stone-formers had significantly lower incidence of hypertension (48.2% vs. 14.3%; p=0.03), lower serum ionized calcium (1.16 vs. 1.07 mmol/L; p=0.01) and magnesium (0.95 vs. 0.81 mmol/L; p=0.01), and significantly higher serum uric acid (281.5 vs. 319.0 μmol/L; p=0.03). Multivariate analysis demonstrated that lower serum ionized calcium (adjusted odds ratio [OR] 0.00001; 95% confidence interval [CI] 0-0.18) and magnesium (adjusted OR 0.0003; 95% CI 0-0.59) were significantly associated with stone-formation. CONCLUSIONS: The incidence of de novo nephrolithiasis in ESRD patients on HD was 10.5%. Increased serum uric acid, decreased serum magnesium and ionized calcium, and absence of hypertension were associated with increased stone-formation in ESRD patients on HD.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".