Effects of Cinacalcet on Fracture Events in Patients Receiving Hemodialysis
Bibliographic record
Abstract
Fractures are frequent in patients receiving hemodialysis. We tested the hypothesis that cinacalcet would reduce the rate of clinical fractures in patients receiving hemodialysis using data from the Evaluation of Cinacalcet HCl Therapy to Lower Cardiovascular Events trial, a placebo-controlled trial that randomized 3883 hemodialysis patients with secondary hyperparathyroidism to receive cinacalcet or placebo for ≤64 months. This study was a prespecified secondary analysis of the trial whose primary end point was all-cause mortality and non-fatal cardiovascular events, and one of the secondary end points was first clinical fracture event. Clinical fractures were observed in 255 of 1935 (13.2%) patients randomized to placebo and 238 of 1948 (12.2%) patients randomized to cinacalcet. In an unadjusted intention-to-treat analysis, the relative hazard for fracture (cinacalcet versus placebo) was 0.89 (95% confidence interval [95% CI], 0.75 to 1.07). After adjustment for baseline characteristics and multiple fractures, the relative hazard was 0.83 (95% CI, 0.72 to 0.98). Using a prespecified lag-censoring analysis (a measure of actual drug exposure), the relative hazard for fracture was 0.72 (95% CI, 0.58 to 0.90). When participants were censored at the time of cointerventions (parathyroidectomy, transplant, or provision of commercial cinacalcet), the relative hazard was 0.71 (95% CI, 0.58 to 0.87). Fracture rates were higher in older compared with younger patients and the effect of cinacalcet appeared more pronounced in older patients. In conclusion, using an unadjusted intention-to-treat analysis, cinacalcet did not reduce the rate of clinical fracture. However, when accounting for differences in baseline characteristics, multiple fractures, and/or events prompting discontinuation of study drug, cinacalcet reduced the rate of clinical fracture by 16%-29%.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".