Specific barriers to the conduct of randomized trials
Bibliographic record
Abstract
Large randomized trials are required to provide reliable evidence of the typically moderate benefit of most interventions. To be affordable, such trials need to be simple; to be widely applicable, they need to be close to normal clinical practice. However, current regulations and guidelines have hugely increased trial complexity, effectively becoming barriers to their design and conduct. Key barriers include inadequate funding, overly complex regulations producing needlessly complex trial procedures, excessive monitoring, over restrictive interpretation of privacy laws without evidence of subject benefit, and inadequate understanding of methodology. Complex regulations result in multiple ethics approvals for a multi-center study, unnecessary complexity in the study protocol, delays in securing regulatory approval, and cumbersome regulatory procedures, even for drugs widely used in clinical practice. The type of detailed safety monitoring currently needed in trials of new drugs is being applied indiscriminately to all studies including a simpler and basic level of monitoring that constitutes good practice in most trials could be agreed on, with that level being exceeded only in specific instances. More evidence about the pros and cons of alternative approaches to data quality monitoring would help inform this process. Complex procedures in the form of multiple-page consent forms, overzealous monitoring of side effects and adverse events, source data verification, and over-restrictive approaches to protocol amendments, can impede, rather than facilitate, trial objectives. Finally, further education on the nuances and functions of randomisation would facilitate trial conduct, and reduce the need for burdensome complexity. A radical re-evaluation of existing trial guidelines is needed, based on a clear understanding of the important principles of randomized trials, with the objective of eliminating unnecessary documentation and reporting without sacrificing validity or safety. Researchers should encourage public debate about how best to strike the balance between regulation and cost.
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 | 0.767 | 0.892 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.014 | 0.015 |
| Research integrity | 0.019 | 0.029 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".