Abstract 391: Variation in Time to Notification After Enrollment in Trials Conducted Under Exception From Informed Consent for Emergency Research
Bibliographic record
Abstract
Context: Research in an emergency setting is challenging because the window of opportunity to treat may be short, and preclude time to obtain informed consent from the patient or their representative. Such research can be conducted under exception from informed consent (EFIC) if specific criteria are met. In the United States, this includes notification of an enrolled subject or their representative as soon as feasible after enrollment so that they have autonomy to opt out from ongoing study participation. To date, there is limited empiric information about time to notification (TTN). Objective: To describe variation in TTN among sites participating in randomized trials conducted under exception from informed consent for emergency research. Methods: Notification strategies were determined at each site prior to initiation of subject enrollment, and approved by a local institutional review board or equivalent. TTN was summarized overall, as well as stratified by site and clinical outcome among patients enrolled in multiple trials conducted by the Resuscitation Outcomes Consortium (ROC). Results: Included were 34,868 patients enrolled in four trials. Of these, 33,805 had with out-of-hospital cardiac arrest; and 1,063 had life-threatening traumatic injury. TTN varied (Table). Conclusions: There is large variation in TTN in trials conducted under EFIC for emergency research. Early notification is difficult; delayed notification may reduce the autonomy of patients or their representative.
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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 | 0.265 | 0.486 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".