Ethics and Equipoise: Rationale for a Placebo‐Controlled Study Design of Platelet Glycoprotein IIb/IIIa Inhibition in Coronary Intervention
Bibliographic record
Abstract
Rarely is it straightforward to specify the design and parameters of a clinical trial investigating an alternative therapy where effective therapies already exist. If existing therapeutic interventions are highly efficacious, safe, inexpensive, and firmly entrenched, an active-control design becomes the logical first choice. Short of this absolute condition, however, the merits and realities of the scientific, clinical, corporate, and regulatory environments need to be weighed before determining the appropriate approach. A state of clinical equipoise with regard to the use of glycoprotein (GP) IIb/IIIa therapy in percutaneous coronary intervention (PCI) provided the unique opportunity to address the complexities in selecting a placebo-controlled design for the Enhanced Suppression of the Platelet IIb/IIIa Receptor with Integrilin Trial (ESPRIT). ESPRIT investigators assessed whether a high dose of eptifibatide would improve the outcomes of patients undergoing coronary stenting. By using the example of the ESPRIT trial, we examine factors warranting the need for this trial and evaluate the process whereby the United States Food and Drug Administration (FDA) gave approval for a placebo-controlled design. Although the focus of this trial is GP IIb/IIIa inhibition therapy, the issues pertaining to the trial and how they were resolved are general enough to be applied to the design and conduct of clinical trials across a broad spectrum of illnesses and therapeutic modalities.
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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 | 0.612 | 0.598 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.036 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".