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U.S. Federal Regulations for Emergency Research: A Practical Guide and Commentary

2008· review· en· W1534297636 on OpenAlexafffund
Andrew D. McRae, Charles Weijer

Bibliographic record

VenueAcademic Emergency Medicine · 2008
Typereview
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsQueen's UniversityWestern University
FundersCanadian Institutes of Health ResearchU.S. Department of Health and Human ServicesU.S. Department of Defense
KeywordsMedicineLibrary scienceMedical emergencyEngineering ethics

Abstract

fetched live from OpenAlex

Emergency medicine research requires the enrollment of subjects with varying decision-making capacities, including capable adults, adults incapacitated by illness or injury, and children. These different categories of subjects are protected by multiple federal regulations. These include the federal Common Rule, the Department of Health and Human Services (DHHS) regulations for pediatric research, and the Food and Drug Administration's (FDA) Final Rule for the Exception from the Requirements of Informed Consent in Emergency Situations. Investigators should be familiar with the relevant federal research regulations to optimally protect vulnerable research subjects, and to facilitate the institutional review board (IRB) review process. IRB members face particular challenges in reviewing emergency research. No regulations exist for research enrolling incapacitated subjects using proxy consent. The wording of the Final Rule may not optimally protect vulnerable subjects. It is also difficult to apply conflicting regulations to a single study that enroll subjects with differing decision-making capacities. This article is intended as a guide for emergency researchers and IRB members who review emergency research. It reviews the elements of Federal Regulations that apply to consent, subject selection, privacy protection, and the analysis of risks and benefits in all emergency research. It explores the challenges for IRB review listed above, and offers potential solutions to these problems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.080
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.305
GPT teacher head0.544
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations16
Published2008
Admission routes2
Has abstractyes

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