Degree of Albuminuria is Associated With Increased Risk of Fragility Fractures Independent of Estimated GFR
Bibliographic record
Abstract
Introduction: Fragility fractures are common in persons with chronic kidney disease (CKD); however, the association between fragility fractures and albuminuria is not well-studied. The primary objective of this study is to determine the association of albuminuria with incident risk of fragility fractures. The secondary objective is to examine the risk of fragility fracture by estimated glomerular filtration rate (eGFR) and Kidney Disease Improving Global Outcomes (KDIGO) risk categories. Methods: = 2.72 million). Incident fragility fractures were identified using Canadian Chronic Disease Surveillance Systems Osteoporosis Working Group algorithms. Albuminuria was categorized as none/mild (albumin-to-creatinine ratio [ACR] <30 mg/g, protein-to-creatinine ratio [PCR] <150 mg/g, trace/negative dipstick); moderate (ACR 30-300 mg/g, PCR 150-500 mg/g, 1+ dipstick) or severe (ACR >300 mg/g, PCR >500 mg/g, ≥2+ dipstick). Multivariable analysis controlled for 42 variables. Results: Patients with severe albuminuria had an increased risk of hip fracture (odds ratio [OR] = 1.37; 95% confidence interval [CI] 1.28, 1.47]), vertebral fracture (OR = 1.31; 95% CI 1.21, 1.41) and any-type fracture (OR = 1.22; 95% CI 1.17, 1.28) compared with patients with none/mild albuminuria. Patients in the most severe KDIGO risk category had an increased risk of hip fracture (OR = 1.22; 95% CI 1.16, 1.29), vertebral fracture (OR = 1.18; 95% CI 1.09, 1.26) and any type of fracture (OR = 1.25; 95% CI 1.21, 1.30). Conclusion: This study demonstrates the important role of albuminuria as a risk factor for fragility fractures in CKD and may help inform risk stratification and prevention strategies in this high-risk population category.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".