Analysis of consent validity for invasive, nondiagnostic research procedures.
Bibliographic record
Abstract
A growing number of clinical trials use invasive research procedures for screening patients, monitoring the effects of drugs, biomarker analysis, or sham comparators. These procedures can be ethically contentious, in part because of concerns about the quality of informed consent provided by patient-volunteers. In the first section of this paper, we describe burdens, risks, and benefits associated with certain common invasive, research procedures. We next offer a series of arguments about the general properties of a valid consent for such procedures. We close by examining what is currently known about consent quality for invasive research procedures against the standards laid out in the second section. We conclude that there is little evidence to either confirm or dispel concerns about consent quality for invasive, nondiagnostic research procedures applied in patient-volunteers.
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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 | 0.744 | 0.906 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".