Practicalities of Impracticability: An Interim Review of Randomized Controlled Trials
Bibliographic record
Abstract
Impracticability is an ethical standard for waiver of informed consent in research. We examine how well the criterion of impracticability appears to have been fulfilled in a set of 36 completed randomized controlled trials (RCTs) that secured consent from some subjects or LARs and employed waivers to enroll others. These trials were identified among 155 RCTs using waivers of consent in a convenience sample drawn from 7 systematic reviews. Recruitment data were available for 19 of the 36 trials, revealing an average of 41.6% of subjects (range 0.2-98.7%, 95% CI: 24.8-58.4%) were enrolled without consent. Six trials enrolled less than 10% of subjects without consent and an overlapping set of 9 trials sought consent from all subjects or LARs at some sites while waiving consent at other sites. We question whether these trials were practicable without waivers and identify issues for consideration by investigators and ethics review boards.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.708 | 0.860 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".