Risk in Emergency Research Using a Waiver of/Exception from Consent: Implications of a Structured Approach for Institutional Review Board Review
Bibliographic record
Abstract
OBJECTIVE: To apply component analysis, a structured approach to the ethical analysis of risks and potential benefits in research, to published emergency research using a waiver of/exception from informed consent. The hypothesis was that component analysis could be used with a high degree of interrater reliability, and that the vast majority of emergency research would comply with a minimal-risk threshold. METHODS: A Medline search and manual search were done to identify studies using a waiver of/exception from informed consent published between July 1996 and December 2000. A review panel of physicians and bioethicists independently classified nontherapeutic procedures in each study as minimal risk, probably minimal risk, or probably more than minimal risk. RESULTS: Seventy studies using a waiver of/exception from informed consent were identified. A majority of reviewers classified nontherapeutic procedures in 62 studies (88.6%) as minimal risk. Reviewers classified nontherapeutic procedures in six studies (8.6%) as minimal risk or probably minimal risk. In two studies (2.9%), nontherapeutic procedures were classified as probably more than minimal risk. The intraclass correlation coefficient was 0.89 (95% CI = 0.85 to 0.93), indicating very high interrater reliability. CONCLUSIONS: Component analysis can be used with high reliability to review emergency research and may improve the consistency of institutional review board review of emergency research. The vast majority of published emergency research performed using a waiver of/exception from consent complies with a properly-applied minimal-risk threshold. A minimal-risk threshold for nontherapeutic procedures protects subjects better than current U.S. regulations while permitting important emergency research to continue.
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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 | 0.914 | 0.949 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.012 | 0.016 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".