Bibliographic record
Abstract
Abstract Informed consent and equipoise are key components in the ethical analysis of clinical trials. However, these concepts were developed during a time when “clinical trial” primarily referred to an individually randomized controlled trial. In the twenty-first century, there has been considerable innovation in trial design. Cluster randomized trials, biomarker-stratified or enrichment trials, and multi-arm platform trials are increasingly used. In this chapter, the authors show how each of these innovative designs raises new challenges for understanding and operationalizing ethical judgments regarding consent and equipoise. The features of innovative designs that drive the need for ethical concepts to evolve and adapt differ. In cluster randomized trials, the need to adapt is driven by the broader scope of such trials in terms of interventions and participants; in biomarker-stratified and enrichment designs, it is the complexity of the scientific hypothesis; and in multi-arm platform designs, it is the evolving set of interventions. Despite the sometimes quite dramatic changes to the structure and hypotheses of randomized controlled trials, the ethical concepts of equipoise and consent remain relevant for all randomized controlled trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.114 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".