Fracture Prediction and Prevention in Individuals with Chronic Kidney Disease
Bibliographic record
Abstract
Patients with chronic kidney disease (CKD) face increased fracture risk yet our understanding and management of this risk remains poor. We conducted three studies using retrospective cohort analysis in Ontario, Canada. We developed a 3-year fracture prediction model for patients receiving dialysis. Secondly, we contrasted fracture rates among patients on SGLT2i or DPP4i medications, stratified by kidney function. Lastly, we examined hypocalcemia incidence post-denosumab prescription, stratified by kidney function.\nFindings: The fracture risk tool, incorporating demographic and lab data, performed well (AUC 0.72). SGLT2i did not elevate fracture risk vs. DPP4i (HR 0.95 [95% CI 0.79,1.13]). In those prescribed denosumab, hypocalcemia occurred in 0.6% overall but increased to 24.1% in those with eGFR/min/1.73m². These studies contribute to our understanding of the causes and prediction of fractures in patients with CKD. Further validation of the risk score and research into the efficacy of denosumab and management of hypocalcemia are warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".