P.035 Using deferred consent in emergency research: an evaluation of two prospective CT-perfusion studies
Bibliographic record
Abstract
Background: Informed consent is not always possible in emergency research particularly during life threatening situations. Deferral of consent is an acceptable method in consenting patients; however, it is underutilized. We aim to share our experience with deferred consent. Methods: Participants in two prospective studies underwent a CT-Perfusion scan (intervention) at the time of first hospital imaging, in order not to impact clinical treatment. Deferred consent was then obtained. The primary outcome was the rate of deferred consent. The number of days to obtain consent, refusal rate, and waiver of consent rate was also reported. Results: A total of 291 patients (200 severe traumatic brain injury [TBI] and 91 out-of-hospital cardiac arrest) were enrolled between the two emergency CT-perfusion studies. Some (34/291[11.9%]) could not be reached; waiver of consent was granted by our ethics board. Deferred consent was obtained in 252/291(86.6%). The majority were consented by the partner/spouse (25.2%) and most consents took place within 7-days (76.0%) of enrollment. Five (1.7%) refused consent. Deferred consent rates were higher in the cardiac arrest population (97.8%) compared to the severe TBI population (83.7%). Conclusions: Deferred consent is an acceptable method of obtaining consent in emergency research when the intervention risk is low.
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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 | 0.043 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".