2024 RECOVER Guidelines: Methods, evidence identification, evaluation, and consensus process for development of treatment recommendations
Bibliographic record
Abstract
OBJECTIVE: To describe the methodology used by the Reassessment Campaign on Veterinary Resuscitation (RECOVER) to re-evaluate the scientific evidence relevant to CPR in small and large animals, to newborn resuscitation, and to first aid and to formulate the respective consensus-based clinical guidelines. DESIGN: This report describes the evidence-to-guidelines process employed by RECOVER that is based on the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach and includes Information Specialist-driven systematic literature search, evidence evaluation conducted by more than 200 veterinary professionals, and provision of clinical guidelines in the domains of Preparedness and Prevention, Basic Life Support, Advanced Life Support, Post-cardiac Arrest Care, Newborn Resuscitation, First Aid, and Large Animal CPR. SETTING: Transdisciplinary, international collaboration in academia, referral practice, and general practice. RESULTS: For this update to the RECOVER 2012 CPR guidelines, we answered 135 Population, Intervention, Comparator, and Outcome (PICO) questions with the help of a team of Domain Chairs, Information Specialists, and more than 200 Evidence Evaluators. Most primary contributors were veterinary specialists or veterinary technician specialists. The RECOVER 2024 Guidelines represent the first veterinary application of the GRADE approach to clinical guideline development. We employed an iterative process that follows a predefined sequence of steps designed to reduce bias of Evidence Evaluators and to increase the repeatability of the quality of evidence assessments and ultimately the treatment recommendations. The process also allowed numerous important knowledge gaps to emerge that form the foundation for prioritizing research efforts in veterinary resuscitation science. CONCLUSIONS: Large collaborative, volunteer-based development of evidence- and consensus-based clinical guidelines is challenging and complex but feasible. The experience gained will help refine the process for future veterinary guidelines initiatives.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".