Advantages and Disadvantages of Using Small and Large Animals in Burn Research: Proceedings of the 2021 Research Special Interest Group
Bibliographic record
Abstract
Multiple animal species and approaches have been used for modeling different aspects of burn care, with some strategies considered more appropriate or translatable than others. On April 15, 2021, the Research Special Interest Group of the American Burn Association held a virtual session as part of the agenda for the annual meeting. The session was set up as a pro/con debate on the use of small versus large animals for application to four important aspects of burn pathophysiology: burn healing/conversion, scarring, inhalation injury, and sepsis. For each of these topics, two experienced investigators (one each for small and large animal models) described the advantages and disadvantages of using these preclinical models. The use of swine as a large animal model was a common theme due to anatomic similarities with human skin. The exception to this was a well-defined ovine model of inhalation injury; both of these species have larger airways which allow for incorporation of clinical tools such as bronchoscopes. However, these models are expensive and demanding from labor and resource standpoints. Various strategies have been implemented to make the more inexpensive rodent models appropriate for answering specific questions of interest in burns. Moreover, modeling burn-sepsis in large animals has proven difficult. It was agreed that the use of both small and large animal models has merit for answering basic questions about the responses to burn injury. Expert opinion and the ensuing lively conversations are summarized herein, which we hope will help inform experimental design of future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".