Tissue Engineering in Wound Repair: The three “R”s—Repair, Replace, Regenerate
Bibliographic record
Abstract
Horses are predisposed to traumatic wounds that can be labor intensive and expensive to manage. Skin has a considerable potential for efficient and functional repair however, while cutaneous repair is a regenerative process in the fetus, this capability declines in late gestation as inflammation and scarring alter the outcome of healing. The historical gold standard for replacement of lost skin is the autologous skin graft. However, the horse's lack of redundant donor skin limits the practicality of full-thickness grafting to smaller wounds; moreover, graft failure is relatively common in equine patients as a result of infection, inflammation, fluid accumulation beneath the graft, and motion. Tissue engineering has emerged as an interdisciplinary field with the aim to regenerate new biological material for replacing diseased or damaged tissues or organs. In the case of skin, the ultimate goal is to rapidly create a construct that effects the complete regeneration of functional skin, including all its layers and appendages. Moreover, an operational vascular and nervous network, with scar-free integration within the surrounding host tissue, is desirable. For this to be achieved, not only is an appropriate source of cells required, but also a scaffold designed from natural or synthetic polymers. The newly created tissue might finally meet the numerous needs and expectations of practitioners and surgeons managing a catastrophic wound in a horse.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".