Informed consent practices in clinical research: present and future
Bibliographic record
Abstract
Clinical research must balance the need for ambitious recruitment with protecting participants' autonomy; a requirement of which is informed consent. Despite efforts to improve the informed consent process, participants are seldom provided sufficient information regarding research, hindering their ability to make informed decisions. These issues are particularly pervasive among patients experiencing acute illness or neurological impairment, both of which may impede their capacity to provide consent. There is a critical need to understand the components, requirements, and methods of obtaining true informed consent to achieve the vast numbers required for meaningful research. This paper provides a comprehensive review of the tenets underlying informed consent in research, including the assessment of capacity to consent, considerations for patients unable to consent, when to seek consent from substitute decision-makers, and consent under special circumstances. Various methods for obtaining informed consent are addressed, along with strategies for balancing recruitment and consent.
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 | 0.139 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".