Abstract 142: Partnering With Survivors and Families to Set Cardiac Arrest Research Priorities: Moving Towards More Patient-Oriented Resuscitation Research
Bibliographic record
Abstract
Background: “Priority setting” is something that we do in research in order to get a range of opinions on what research questions are the most important or timely in a particular field. This is typically done with a group of expert health care providers and researchers and has not involved patients, family or the public. Methods: With funding from the Canadian Institutes of Health Research we partnered with the James Lind Alliance (JLA) in the UK to use their proven consensus methodology to build a Public Priority Setting Partnerships (PSP) on out-of-hospital Cardiac Arrest (OHCA). We developed and administered two rounds of online surveys with survivors, family members and health care professionals to create the initial priority lists. From there we held a full-day in-person workshop to come to consensus on the top 10 priorities from a short list of 25. The workshop also included survivors, family members and health care professionals. Results: The initial survey yielded more than 425 responses and 1500 “questions”/comments from survivors, family members, lay responders, physicians, nurses, rehabilitation specialists, paramedics, etc. The second survey asked participants to rank a narrowed list of 125 questions. The final top 25 questions were brought to the in-person consensus meeting and a top 10 were selected. The results will be revealed publicly for the first time at the ReSS meeting. Conclusions: The JLA methodology is world-renowned for its rigor and we now have an official PSP on OHCA. This type of inclusive research priority setting provides crucial information for the AHA and the international resuscitation research community about where to invest our limited research resources. The results will also drive our community to include questions that matter to SCA survivors and their families in all of our work.
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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 | 0.242 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".