Lessons Learned from EVOLVE for Planning of Future Randomized Trials in Patients on Dialysis
Bibliographic record
Abstract
The effect of the calcimimetic cinacalcet on cardiovascular disease in patients undergoing hemodialysis with secondary hyperparathyroidism was assessed in the Evaluation of Cinacalcet Hydrochloride Therapy to Lower Cardiovascular Events trial. This was the largest (in size) and longest (in duration) randomized controlled clinical trial undertaken in this population. During planning, execution, analysis, and reporting of the trial, many lessons were learned, including those related to the use of a composite cardiovascular primary endpoint, definition of endpoints (particularly heart failure and severe unremitting hyperparathyroidism), importance of age for optimal stratification at randomization, use of unadjusted and adjusted intention-to-treat analysis for the primary outcome, how to respond to a lower-than-predicted event rate during the trial, development of a prespecified analytic plan that accounted for nonadherence and for cointerventions that diminished the power of the trial to observe a treatment effect, determination of the credibility of a subgroup effect, use of adverse effects database to investigate rare diseases, collection of blood for biomarker measurement not designated before trial initiation, and interpretation of the benefits-to-harms ratio for individual patients. It is likely that many of these issues will arise in the planning of future trials in CKD.
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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 | 0.242 | 0.315 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".