Effects of denosumab on bone mineral density and bone metabolism in patients with end‐stage renal disease: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: The effects of denosumab on bone mineral density (BMD) and metabolism in patients with end-stage renal disease (ESRD) remain controversial. Hence, we performed a systematic review and meta-analysis of observational studies. METHODS: The MEDLINE, EMBASE, and Cochrane Library databases were searched in June 2022 to identify studies that evaluated the risk of denosumab-associated hypocalcemia and changes in bone metabolism, changes in BMD from baseline to post-treatment in patients with ESRD. FINDINGS: = 63.6%). There were no significant changes in either the serum calcium or phosphate levels from the baseline to post-treatment period; the mean differences were 0.04 mg/dL (95% CI, -0.12 to 0.20 mg/dL) and -0.39 mg/dL (95% CI, -0.89 to 0.12 mg/dL). We found significant changes in the alkaline phosphatase and parathyroid hormone levels; the standardized mean differences were -2.98 (95% CI, -5.36 to -0.59) and -3.12 (95% CI: -4.94 to -1.29), respectively. Denosumab may increase BMD, with mean differences of 9.10% (95% CI: 4.07%-14.13%) and 9.00% (95% CI: 5.93%-12.07%) for the femoral neck and lumbar spine, respectively. DISCUSSION: Denosumab increased the BMDs of the lumbar spine and femoral neck in patients with ESRD. The onset of hypocalcemia must be carefully monitored during denosumab administration.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".