Incidence of fractures in middle-aged individuals with early chronic kidney disease: a population-based analysis of CARTaGENE
Bibliographic record
Abstract
BACKGROUND: Previous studies evaluating fractures in chronic kidney disease (CKD) have mostly focused on hip or major fractures in aged populations with moderate to advanced CKD. We aimed at evaluating the association between early CKD and fracture incidence at all sites across age and sex in middle-aged individuals. METHODS: We analyzed CARTaGENE, a prospective population-based survey of 40- to 69-year-old individuals from Quebec (Canada). Estimated glomerular filtration rate (eGFR) at baseline was evaluated categorically or continuously using restricted cubic splines. Fractures at any site (except toes, hand and craniofacial) for up to 7 years of follow-up were identified through administrative databases using a validated algorithm. Adjusted Cox models were used to evaluate the association of CKD with fracture. Interaction terms for age and sex were also added. RESULTS: A total of 19 391 individuals (756 CKD Stage 3; 9114 Stage 2; 9521 non-CKD) were included and 829 fractures occurred during a median follow-up of 70 months. Compared with the median eGFR of 90 mL/min/1.73 m2, eGFRs of ≤60 mL/min/1.73 m2 were associated with increased fracture incidence in unadjusted and adjusted models [adjusted hazard ratio (HR) = 1.25 (95% confidence interval 1.05-1.49) for 60 mL/min/1.73 m2; 1.65 (1.14-2.37) for 45 mL/min/1.73 m2]. The eGFR was linearly associated with fracture incidence <75 mL/min/1.73 m2 [HR = 1.18 (1.04-1.34) per 10 mL/min/1.73 m2 decrease] but not above [HR = 0.98 (0.91-1.06) per 10 mL/min/1.73 m2 decrease). The effect of decreased eGFR on fracture incidence was more pronounced in younger individuals [HR = 2.45 (1.28-4.67) at 45 years; 1.11 (0.73-1.67) at 65 years] and in men. CONCLUSIONS: Even early CKD increases fracture incidence, especially in younger individuals and in men.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".