Experimental Keloid Scar Models: A Review of Methodological Issues
Bibliographic record
Abstract
Background: Keloid scars are benign fibrous proliferations in the dermis that arise after dermal trauma. The scars are raised in appearance and extend beyond the boundaries of the original wound. Scarring in predisposed individuals is out of proportion to the severity of the inciting wound. Current treatments sometimes yield early benefit but scars often resume exuberant growth. The pathophysiology of keloid scars is still poorly understood. In order for new treatments to be developed, the mechanisms leading to the formation of keloid scars must be further elucidated. The search for improved experimental models is of critical importance because such models have an important role to play in both the study of keloid formation and in the development of new therapies. Objective: The objective of this article is to introduce the reader to the experimental models available for studying keloid scars and to outline the advantages and limitations of animal and tissue culture models. Conclusion: Both models may help to elucidate the pathways of keloid formation and promote development and testing of therapies. Tissue culture is better suited to studies of pathogenesis, whereas the animal models are more suitable for therapeutic testing.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".