Bibliographic record
Abstract
The Wolf Creek Conference, initiated in 1975, provides a unique forum for robust intellectual exchange between thought leaders and scientists from academia and industry focused on advancing the science and practice of cardiac arrest resuscitation. The 50th year anniversary Wolf Creek XVIII Conference was hosted by the Max Harry Weil Institute for Critical Care Research and Innovation in Ann Arbor, Michigan, USA, on June 19-21, 2025. A major focus of the conference proceedings was to identify and prioritize knowledge gaps, barriers to translation, and research priorities for six major domains in the field of resuscitation: (1) optimizing time intervals in cardiac arrest care, (2) innovations in defibrillation science, (3) innovations in extracorporeal cardiopulmonary resuscitation (ECPR) technology, (4) cardiac arrest survivorship science, (5) transforming clinical trial design in cardiac arrest research, and (6) strategies to optimize international collaboration in cardiac arrest research. In addition, industry scientists and academic investigators were given the opportunity to present and discuss cutting edge innovations. Finally, the "Wolf Creek Innovator Award" competition recognized early career investigators who were challenging current paradigms in resuscitation science. The overall goal was to fuel active discussion and debate among emerging and established experts and steer the future direction of research efforts in the field. This manuscript provides an overview of the conference, which is expanded upon in the individual manuscripts within this special edition of Resuscitation Plus. The intent of these publications is to provide a roadmap for impactful academic and commercial advances in the field of cardiac arrest resuscitation.
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 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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.109 | 0.084 |
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".