Bibliographic record
Abstract
Until recently, the gold standard for assessing the efficacy and effectiveness of new medications has been the placebo-control randomized clinical trial (RCT). However, there are serious ethical concerns about placing patients on a placebo when effective treatments exist. Further, if a new agent is tested only against a placebo, there is no guarantee that it is more effective, or even as effective, as an existing agent. For these and other reasons, ethicists and regulatory bodies have said that, under these circumstances, new drugs should be tested against an active agent. There are three types of such trials: superiority, equivalence, and non-inferiority. In superiority trials, the goal is to establish that the new drug is better (i.e., more effective, or with a more benign side-effect profile) than the standard. Because such trials require much larger sample sizes than placebo-control studies, and are rarely required to bring a drug onto market, they are rarely done. In equivalence trials, the aim is to show that the new and standard agents have similar degrees of effectiveness or adverse events. Due to sample size requirements, most studies of new drugs are non-inferiority trials, in which it is sufficient to demonstrate that the new drug is not significantly worse than the existing ones. However, there are methodological concerns with equivalence and non-inferiority trials, including (a) an inability to determine if the drugs were equally good or equally bad; (b) poorly executed trials with low power can be mistaken for "proving" equivalence or non-inferiority; (c) the equivalence interval is arbitrary; (d) successive non-inferiority trials may lead to a gradual reduction in effectiveness; and (e) often larger trials are necessary. The paper also discusses "add on trials." It is recommended that, even when existing drugs exist, trials should consist of at least three arms, one of which is a placebo. This paper briefly considers the ethics of placebo, and conditions are stated under which such studies can be conducted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.094 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.008 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".