Preliminary Experience With Social Media for Community Consultation and Public Disclosure in Exception From Informed Consent Trials
Bibliographic record
Abstract
Social Media as a Tool in Medicine 267 E ach year in the United States, >300 000 individuals suffer cardiopulmonary arrest and >2 000 000 suffer a serious traumatic injury requiring hospitalization.1,2 Clinical advances in these fields have been limited because of the paucity of scientific evidence guiding resuscitation practices.Prospective clinical trials are important for advancing the knowledge base in these areas, but because of the emergent nature of these conditions and the inability of critically ill subjects to provide informed consent, investigators must often conduct clinical trials of cardiac arrest and trauma under federal regulations for Exception From Informed Consent (EFIC).3 Editorial see p 206The US Department of Health and Human Services and the Food and Drug Administration have issued guidelines for the execution of clinical studies using the EFIC process.3 One of the key steps for EFIC research is community consultation and public disclosure (CC/PD), a process that connotes consultation between the investigative team, the appropriate institutional review boards, representatives of the communities where the research will be conducted, and communities likely to have the condition.However, the regulations provide little guidance on how to accomplish these key goals.Although traditional methods for CC/PD include public meetings and telephone-based surveys, the ability of these modalities to access the target populations, to effectively deliver information, and to elicit useful feedback remains unclear.4 Internet-based social media has emerged as a popular and powerful new modality for communication internationally.The objective of this study was to describe our preliminary experience using social media to facilitate the CC/PD process for cardiopulmonary arrest and trauma trials using EFIC. Methods Study DesignThe activities for this study, including the use of social media for CC/PD, were approved by the Institutional Review Board of the University of Alabama at Birmingham.We devised a social media interface to facilitate the CC/PD process for cardiac arrest and major trauma trials conducted at the Alabama site of the Resuscitation Outcomes Consortium (ROC). Study SettingROC is a collaboration of 10 sites in the United States and Canada dedicated to the study of out-of-hospital cardiac arrests and severe traumatic injury.5 The ROC catchment area includes a population of 23.7 million people over a coverage area of 35 500 sq miles and treats ≈11 900 nontraumatic emergency medical services-treated out-of-hospital cardiac arrests per year.6 The Alabama ROC site includes 10 emergency medical services agencies in the greater Birmingham, AL, community, encompassing a population of 650 000 people over 1300 sq miles and served by >1400 emergency medical services personnel.Social media as CC/PD was used for 2 ROC clinical trials.The ROC Continuous Chest Compressions (CCC) Trial is a multicenter, cluster, randomized trial comparing the 2 strategies of delivering cardiopulmonary resuscitation chest compressions to victims of outof-hospital cardiac arrests: continuous chest compressions and traditional 30:2 interrupted chest compressions.4 The ROC Hypotensive Resuscitation (HYPO Resus) Trial is a prospective, randomized trial comparing standard and limited volume crystalloid fluid resuscitation in victims of hemorrhagic shock.
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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 | 0.090 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".