RECOVER Guidelines: Newborn Resuscitation in Dogs and Cats. Evidence and Knowledge Gap Analysis With Treatment Recommendations
Bibliographic record
Abstract
OBJECTIVE: To systematically review the evidence on, to devise clinical recommendations for, and to identify critical knowledge gaps in resuscitation of newborn puppies and kittens. DESIGN: Standardized, systematic evaluation of literature pertinent to newborn resuscitation following Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. Prioritized questions were reviewed by Evidence Evaluators, and findings were reconciled by Domain Chairs and Reassessment Campaign on Veterinary Resuscitation (RECOVER) Co-Chairs to arrive at treatment recommendations commensurate with the 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. Treatment recommendations underwent a modified Delphi consensus process and were then distributed to veterinary professionals for comment for 2 weeks prior to finalization. SETTING: Transdisciplinary, international collaboration in university, specialty, and emergency veterinary practice. RESULTS: Twenty-eight questions pertaining to temperature management, respiratory and metabolic support, and CPR were addressed. Of the 59 treatment recommendations formulated, 21 concerned medications, 20 addressed respiratory measures, 20 provided guidance on CPR, and 3 related to temperature management. Taken together, the recommendations emphasize the importance of early administration of bag-mask ventilation in nonvigorous, severely bradycardic newborn puppies and kittens. Most recommendations are either expert opinion (n = 28) or based on very low quality of evidence (n = 26). CONCLUSIONS: Significant uncertainty remains regarding most resuscitative interventions in newborn puppies and kittens at birth. However, through a comprehensive evaluation of the evidence and a consensus process that included considerations of feasibility, the resulting treatment recommendations lay the foundation for clear, actionable guidance in small animal newborn resuscitation. In addition, a list of prioritized knowledge gaps was identified to guide collaborative clinical research to overcome the significant lack of veterinary scientific data at present.
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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 | 0.098 | 0.295 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.020 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".