Randomized Trials in Vulnerable Populations
Bibliographic record
Abstract
Many persons enrolled in clinical trials can be considered vulnerable, and such trials often raise concerns because of the diminished ability of vulnerable persons to consider and protect their own interests. However, this research is necessary to answer important questions, such as which interventions are effective, which have no impact, and which do more harm than good. In this article, we identified six specific challenges associated with randomized clinical trials in vulnerable populations and have suggested several potential solutions to overcome these challenges. First addressed were macro issues, such as the scope of the problem, and research capacity in terms of funding and investigators. Next, we have addressed research ethics review, informed consent, regulatory hurdles, and serious adverse event reporting. As clinical trials are expanding globally, all stakeholders (investigators, granting agencies, REBs, DSMBs, regulatory bodies, universities, hospitals, clinicians, patients, and family members) should be aware of the challenges we have outlined, and work collaboratively toward effective solutions that improve the quality, quantity, safety, and relevance of clinical trials for vulnerable persons around the world.
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 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 | 0.424 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".