How cutting-edge trial design can assess outcomes
Bibliographic record
Abstract
PURPOSE OF REVIEW: Randomized clinical trials (RCTs) have come to be accepted as the gold standard for assessing the efficacy and effectiveness of therapeutics and interventions in medicine. In this paper, we aim to describe some evolving concepts associated with the design and conduct of RCTs and outline new approaches aiming to increase efficiency and reduce costs. RECENT FINDINGS: A well-powered and performed RCT is usually a study involving several different centers from different geographical areas that enrolls a large number of patients in diverse clinical settings. Altogether, these features increase the generalizability of the study and make the rapid implementation of the findings more likely. However, this does not come without cost. Among several possible alternatives to conventional RCTs, the most important ones are related to the unit of randomization (individual vs. cluster), study design (conventional vs. adaptive), randomization scheme (fixed vs. response-adaptive), data collection (conventional case report forms vs. registry-embedded) and statistical approach (frequentist vs. Bayesian). SUMMARY: While conventional RCTs remain the gold standard for generating evidence, new trial designs may be considered to reduce sample size and costs while improving trial efficiency and power. However, they raise new challenges for testing feasibility, conduct, ethical oversight and statistical analysis.
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 | 0.262 | 0.604 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".