Reassessment Campaign on Veterinary Resuscitation (RECOVER) Initiative Small Animal CPR Registry Report 2025
Bibliographic record
Abstract
OBJECTIVE: To provide an update on data in the RECOVER small animal CPR registry and to describe the cardiopulmonary arrest (CPA) and CPR event data collected since registry implementation in 2016. DESIGN: International, multi-institutional veterinary CPR registry data report. SETTING: Twelve veterinary private practices and 10 university teaching hospitals. ANIMALS: Data from 1207 dogs and 458 cats undergoing CPR entered into the RECOVER CPR registry between February 2016 and January 2025. INTERVENTIONS: The RECOVER CPR registry is a multi-institutional online database designed for the standardized collection of CPA and CPR event data in dogs and cats. Records of all cases entered since its implementation in 2016 were downloaded, and summary data were provided after removing test, duplicate, and incomplete entries. MEASUREMENTS AND MAIN RESULTS: Twenty-two hospitals in North America (73%), Europe (14%), Australia (9%), and Asia (5%) contributed data on 1665 CPR events, which represents a 38% increase in registry contributors and 134% increase in entered cases in the reporting interim. Return of spontaneous circulation (ROSC) was achieved in 470 (28%) animals: 341 (28%) dogs and 129 (28%) cats. Sustained ROSC was observed in 189 (16%) dogs and 77 (17%) cats, and 44 (4%) dogs and 12 (3%) cats survived to discharge. Euthanasia remained the most common cause of death in 72% of dogs and 70% of cats that achieved sustained ROSC but did not survive to discharge. CONCLUSION: The RECOVER CPR registry has grown consistently since inception. Its data have contributed to multiple publications, enhancing our understanding of the epidemiology and outcomes of CPA in dogs and cats. This larger data set further corroborates the finding that good outcomes are infrequent, highly variable by setting (e.g., anesthesia), and influenced by modifiable factors (e.g., euthanasia), underscoring the need for continued large-scale, collaborative studies to identify factors associated with improved survival.
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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 | 0.032 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".