Defining sepsis in small animals
Bibliographic record
Abstract
OBJECTIVE: To discuss the definitions of sepsis in human and veterinary medicine. DESIGN: International, multicenter position statement on the need for consensus definitions of sepsis in veterinary medicine. SETTING: Veterinary private practice and university teaching hospitals. ANIMALS: Dogs and cats. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Sepsis is a life-threatening condition associated with the body's response to an infection. In human medicine, sepsis has been defined by consensus on 3 occasions, most recently in 2016. In veterinary medicine, there is little uniformity in how sepsis is defined and no consensus on how to identify it clinically. Most publications rely on modified criteria derived from the 1991 and 2001 human consensus definitions. There is a divergence between the human and veterinary descriptions of sepsis and no consensus on how to diagnose the syndrome. This impedes research, hampers the translation of pathophysiology insights to the clinic, and limits our abilities to optimize patient care. It may be time to formally define sepsis in veterinary medicine to help the field move forward. In this narrative review, we present a synopsis of prior attempts to define sepsis in human and veterinary medicine, discuss developments in our understanding, and highlight some criticisms and shortcomings of existing schemes. CONCLUSIONS: This review is intended to serve as the foundation of current efforts to establish a consensus definition for sepsis in small animals and ultimately generate evidence-based criteria for its recognition in veterinary clinical practice.
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 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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".