Many randomized clinical trials may not be justified: a cross-sectional analysis of the ethics and science of randomized clinical trials
Bibliographic record
Abstract
OBJECTIVE: We have proposed that three scientific criteria are important for the ethical justification of randomized clinical trials (RCTs): (1) they should be designed around a clear hypothesis; (2) uncertainty should exist around that hypothesis; (3) that uncertainty should be as established through a systematic review. We hypothesized that the majority of a sample of recently published RCTs would not explicitly incorporate these criteria, therefore rendering them potentially unjustified on scientific grounds. STUDY DESIGN AND SETTING: Cross-sectional analysis of all RCTs published in the New England Journal of Medicine and the Journal of the American Medical Association in 2015. Each article and protocol was reviewed for: (1) a clearly stated central hypothesis; (2) references to "equipoise," or "consensus;" (3) some indication of evidentiary uncertainty; (4) a meta-analysis or systematic review surrounding the hypothesis or study question. RESULTS: We included 208 RCT articles and 199 protocols. Among combined articles and protocols, 76% had a clearly stated hypothesis, 99% referenced some form of uncertainty, and 54% cited a relevant systematic review or meta-analysis. Only 44% of combined texts contained all three scientific criteria. There were few references to "equipoise" (10%) or "consensus" (11%), and those references to equipoise were most often inconsistent with accepted definitions. CONCLUSION: The majority of RCTs (56%) did not meet the three scientific criteria described previously and therefore may be scientifically and therefore ethically unjustified. We recommend that "equipoise," "clinical equipoise," and "lack of consensus" be abandoned as scientific criteria for RCTs and be replaced by an expectation that RCTs have a clearly stated, meaningful hypothesis around which uncertainty has been established through a systematic review of the literature.
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 | 0.776 | 0.901 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".