Effects of denosumab on fracture and bone mineral density by level of kidney function
Bibliographic record
Abstract
The incidences of osteoporosis and chronic kidney disease (CKD) both increase with increasing age, yet there is a paucity of data on treatments for osteoporosis in the setting of impaired kidney function. We examined the efficacy and safety of denosumab (DMAb) among subjects participating in the Fracture Reduction Evaluation of Denosumab in Osteoporosis Every 6 Months (FREEDOM) Study. We estimated creatinine clearance (eGFR) using Cockcroft-Gault and classified levels of kidney function using the modified National Kidney Foundation classification of CKD. We examined incident fracture rates; changes in bone mineral density (BMD), serum calcium, and creatinine; and the incidence of adverse events after 36 months of follow-up in subjects receiving DMAb or placebo, stratified by level of kidney function. We used a subgroup interaction term to determine if there were differences in treatment effect by eGFR. Most (93%) women were white, and the mean age was 72.3 ± 5.2 years; 73 women had an eGFR of 15 to 29 mL/min; 2817, between 30 to 59 mL/min; 4069, between 60 to 89 mL/min, and 842 had an eGFR of 90 mL/min or greater. None had stage 5 CKD. Fracture risk reduction and changes in BMD at all sites were in favor of DMAb. The test for treatment by subgroup interaction was not statistically significant, indicating that treatment efficacy did not differ by kidney function. Changes in creatinine and calcium and the incidence of adverse events were similar between groups and did not differ by level of kidney function. It is concluded that DMAb is effective at reducing fracture risk and is not associated with an increase in adverse events among patients with impaired kidney function.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".