RECOVER Guidelines: Newborn Resuscitation in Dogs and Cats. Clinical Guidelines
Bibliographic record
Abstract
OBJECTIVE: To present evidence- and consensus-based guidelines for resuscitation of newborn puppies and kittens. DESIGN: Prioritized clinical questions pertaining to newborn resuscitation and in the Population-Intervention-Comparator-Outcome (PICO) format were used to inform systematic literature searches by information specialists, to extract research findings from relevant publications and synthesize them into evidence, to assess this evidence for quality, and, finally, to develop draft treatment recommendations. These steps were followed by a consensus process and a community commenting period prior to finalization of the project. These RECOVER Newborn Resuscitation Guidelines are a concise summary of the newborn resuscitation process to provide clear and actionable clinical instructions to veterinary professionals. SETTING: Transdisciplinary, international collaboration in university, specialty, and emergency practice. RESULTS: A total of 28 PICO questions pertaining to resuscitation of puppies and kittens at birth were addressed in this project. This resulted in 59 treatment recommendations that delineate an iterative approach to newborn resuscitation starting with airway clearance, tactile stimulation, and temperature control, as well as positive pressure ventilation, and instruct on more advanced measures such as CPR. An algorithm displays the flow of assessments and actions over the course of the resuscitation process. CONCLUSIONS: These RECOVER Newborn Resuscitation Guidelines present a concise and comprehensive framework for resuscitation of puppies and kittens at birth. These works serve to support veterinary professionals and breeders, educational systems, and research initiatives in conducting, implementing, and advancing newborn resuscitation in puppies and kittens.
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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 | 0.034 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".