Model of radiation‐impaired healing of a deep excisional wound
Bibliographic record
Abstract
Despite many well-recognized benefits, administration of ionizing radiation before surgical resection of malignancies is associated with a high risk of wound-healing complications. Most animal models investigating techniques to improve wound healing use a superficial wound. The goal of this study was to develop a novel model of radiation-impaired healing using a deep excisional wound, which is closer to the clinical situation. In the first part of this study, female Lewis rats were exposed to 0, 12, 15, or 18 Gy single-fraction radiation to the buttocks. Three weeks later, deep wounds were created by excision of the gluteus maximus muscle. Irradiated wounds had a lower rate of healing of the surgically created defect than unirradiated wounds (p<0.001), but there was no significant difference between the different doses of radiation. Impaired healing was still evident at 12 weeks. The second part of this study investigated the ability of porcine small-intestinal submucosa (SIS) to improve healing in this animal model. At 6 weeks, wounds implanted with SIS showed improved healing at all doses of radiation compared with unimplanted irradiated wounds. However, higher doses of radiation were still associated with a lower rate of healing. SIS induced a cellular response that was not evident in defects that did not receive SIS, suggesting that SIS has the potential to stimulate repair. This reproducible model of radiation-impaired wound healing closely resembles the clinical setting. The results indicate that this model can be used to investigate new biomaterials as possible therapeutic agents to enhance wound healing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".