The effect of anatomic location on porcine models of burn injury and wound healing
Bibliographic record
Abstract
Porcine models are frequently used for burn healing studies; however, factors including anatomic location and lack of standardised wound methods can impact the interpretation of wound data. The objectives of this study are to examine the influence of anatomical locations on the uniformity of burn creation and healing in porcine burn models. To optimise burn parameters on dorsal and ventral surfaces, ex vivo and in situ euthanized animals were first used to examine the location-dependence of the burn depth and contact time relationship. The location-dependent healing in vivo was then examined using burn and excisional wounds at dorsal, ventral, caudal and cranial locations. Lactate dehydrogenase (LDH) and H&E were used to assess burn depth and wound re-epithelialization. We found that burn depth on the ventral skin was significantly deeper than that of the dorsal skin at identical thermal conditions. Compared with burns created ex vivo, burns created in situ immediately post-mortem were significantly deeper in the ventral location. In live animals, 2 out of 12 burn wounds were fully re-epithelialized after 14 days in contrast to complete re-epithelialization of all excisional wounds. Among the burn wounds, those at the cranial-dorsal site exhibited faster healing than at the caudal-dorsal site. This study showed that anatomical location is an important consideration for the consistency of burn depth creation and healing. These data support symmetric localization of treatment and control for comparative assessment of burn healing in porcine models to prevent misinterpretation of results and increase the translatability of findings to humans.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".