Rationale - Trial to Reduce Cardiovascular Events with Aranesp Therapy (TREAT): Evolving the management of cardiovascular risk in patients with chronic kidney disease
Bibliographic record
Abstract
Background Patients with chronic kidney disease (CKD) have a high burden of mortality and cardiovascular morbidity. Additional strategies to modulate cardiovascular risk in this population are needed. Data suggest that anemia is a potent and potentially modifiable risk factor for cardiovascular disease in patients with CKD, but these data remain unsubstantiated by any randomized controlled trial (RCT). Furthermore, the clinical practice guidelines for anemia management in patients with CKD are based on limited data. The need for new RCTs to address critical knowledge deficits, particularly with regard to the impact of anemia therapy on cardiovascular disease and survival, is recognized within the guidelines and independent comprehensive reviews of the existing published trial data. Study Design The Trial to Reduce Cardiovascular Events with Aranesp (Amgen Inc, Thousand Oaks, Calif) (darbepoetin alfa) Therapy (TREAT) is a 4000-patient, multicenter, double-blind RCT, designed to determine the impact of anemia therapy with darbepoetin alfa on mortality and nonfatal cardiovascular events in patients with CKD and type 2 diabetes mellitus. Subjects will be randomized in a 1 : I manner to either darbepoetin alfa therapy to a target hemoglobin (Hb) of 13 g/dL or control, consisting of placebo for Hb >= 9 g/dL or darbepoetin alfa for Hb = 9 g/dL. TREAT is event-driven and has a composite primary end point comprising time to mortality and nonfatal cardiovascular events, including myocardial infarction, myocardial ischemia, stroke, and heart failure. TREAT will provide data that are critical to evolution of the management of cardiovascular risk in this high-risk population.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".