Effects of calcimimetics on long-term outcomes in dialysis patients: literature review and Bayesian meta-analysis
Bibliographic record
Abstract
AIM: Randomized controlled trials (RCTs) with clinical outcomes are considered the gold standard for regulatory approval. However, by design they are only able to answer a small number of clinical questions. Other high-quality studies are required for clinical decision-making. The EVOLVE was the largest RCT, evaluating the effects of cinacalcet on clinical outcomes among adult patients receiving maintenance dialysis suffering from secondary hyperparathyroidism. While the EVOLVE trial did not reach its primary end point, imbalance in subjects' age at randomization and discontinuation rates are two of the reasons that the lack of mortality benefit is in question. We undertook a systematic literature review and Bayesian meta-analysis combining randomized and observational studies on the estimated effects of the oral calcimimetic cinacalcet on clinical outcomes including all-cause mortality, cardiovascular-related mortality, hospitalization for cardiovascular events, fracture and parathyroidectomy among patients on maintenance dialysis. METHODS: Data sources included MEDLINE, EMBASE and Cochrane Central Register of Controlled Trials databases. RCTs and observational studies were included. Data extraction was completed by two authors independently and in duplicate determined the methodological quality of the studies and extracted data. RESULTS: Of 564 unique citations identified, 16 studies were included: six observational studies and ten RCTs. Four high-quality studies (two observational and two RCTs) were deemed suitable for meta-analysis. Results indicated a statistically significant reduction in the risk of death associated with cinacalcet (hazard ratio: 0.83; 95% credible interval: 0.78-0.89). CONCLUSION: The results of this meta-analysis indicate that treatment of secondary hyperparathyroidism with calcimimetic therapy may in fact reduce mortality among patients receiving maintenance dialysis. This finding provides justification for a well-designed and adequately powered randomized trial to definitively address the question.
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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 | 0.039 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".