Rationale and Design of an Adaptive Phase 2b/3 Clinical Trial of Selepressin for Adults in Septic Shock. Selepressin Evaluation Programme for Sepsis-induced Shock—Adaptive Clinical Trial
Bibliographic record
Abstract
receptor agonist, for adults with septic shock. SEPSIS-ACT (Selepressin Evaluation Programme for Sepsis-induced Shock-Adaptive Clinical Trial) is a blinded, randomized, placebo-controlled, two-part, adaptive phase 2b/3 trial, evaluating up to four selepressin dosing strategies. The primary outcome is pressor- and ventilator-free days, with a value of zero assigned for death within 30 days. We calculate Bayesian probabilities of final trial success to guide interim decision-making. Part 1 (dose-finding) has an adaptive sample size based on response-adaptive randomization and prespecified rules to determine stopping for futility or selection of the best dosing regimen for Part 2. Part 2 (confirmation) randomizes a minimum of 1,000 patients equally to the selected dosing regimen or placebo. The final estimate of treatment effect compares all selepressin-treated patients with all placebo-treated patients. The sample size of 1,800 provides 91% power to detect an increase of 1.5 pressor- and ventilator-free days with a reduction in mortality of 1.5%. The trial received a Special Protocol Assessment agreement from the U.S. Food and Drug Administration Center for Drug Evaluation and Research and is underway in Europe and the United States. SEPSIS-ACT is an innovative trial that addresses both optimal dose and confirmation of benefit, accelerating the evaluation of selepressin while mitigating risks to patients and sponsor through use of response-adaptive randomization, a novel registration endpoint, prespecified futility stopping rules, and a large sample size. Clinical Trial registered with www.clinicaltrials.gov (NCT02508649).
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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 | 0.109 | 0.088 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".