Spontaneous Sepsis in Adult Horses: From Veterinary to Human Medicine Perspectives
Bibliographic record
Abstract
Sepsis is a life-threatening disease defined as an organ dysfunction caused by a dysregulated host response to an infection. Early diagnosis and prognosis of sepsis are necessary for specific and timely treatment. However, no predictive biomarkers or therapeutic targets are available yet, mainly due to the lack of a pertinent model. A better understanding of the pathophysiological mechanisms associated with sepsis will allow for earlier and more appropriate management. For this purpose, experimental models of sepsis have been set up to decipher the progression and pathophysiology of human sepsis but also to identify new biomarkers or therapeutic targets. These experimental models, although imperfect, have mostly been performed on a murine model. However, due to the different pathophysiology of the species, the results obtained in these studies are difficult to transpose to humans. This underlines the importance of identifying pertinent situations to improve patient care. As humans, horses have the predisposition to develop sepsis spontaneously and may be a promising model for spontaneous sepsis. This review proposes to give first an overview of the different animal species used to model human sepsis, and, secondly, to focus on adult equine sepsis as a spontaneous model of sepsis and its potential implications for human and veterinary medicine.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".