2024 RECOVER Guidelines: Monitoring. Evidence and knowledge gap analysis with treatment recommendations for small animal CPR
Bibliographic record
Abstract
Abstract Objective To systematically review evidence on and devise treatment recommendations for patient monitoring before, during, and following CPR in dogs and cats, and to identify critical knowledge gaps. Design Standardized, systematic evaluation of literature pertinent to peri‐CPR monitoring following Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. Prioritized questions were each reviewed by Evidence Evaluators, and findings were reconciled by Monitoring Domain Chairs and Reassessment Campaign on Veterinary Resuscitation (RECOVER) Co‐Chairs to arrive at treatment recommendations commensurate to quality of evidence, risk:benefit relationship, and clinical feasibility. This process was implemented using an Evidence Profile Worksheet for each question that included an introduction, consensus on science, treatment recommendations, justification for these recommendations, and important knowledge gaps. A draft of these worksheets was distributed to veterinary professionals for comment for 4 weeks prior to finalization. Setting Transdisciplinary, international collaboration in university, specialty, and emergency practice. Results Thirteen questions pertaining to hemodynamic, respiratory, and metabolic monitoring practices for identification of cardiopulmonary arrest, quality of CPR, and postcardiac arrest care were examined, and 24 treatment recommendations were formulated. Of these, 5 recommendations pertained to aspects of end‐tidal CO 2 (ET co 2 ) measurement. The recommendations were founded predominantly on very low quality of evidence, with some based on expert opinion. Conclusions The Monitoring Domain authors continue to support initiation of chest compressions without pulse palpation. We recommend multimodal monitoring of patients at risk of cardiopulmonary arrest, at risk of re‐arrest, or under general anesthesia. This report highlights the utility of ET co 2 monitoring to verify correct intubation, identify return of spontaneous circulation, evaluate quality of CPR, and guide basic life support measures. Treatment recommendations further suggest intra‐arrest evaluation of electrolytes (ie, potassium and calcium), as these may inform outcome‐relevant interventions.
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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 | 0.099 | 0.278 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".