Effects of a Cross‐linked Hyaluronic Acid Based Gel on the Healing of Open Wounds in Dogs
Bibliographic record
Abstract
OBJECTIVE: To compare effects of a cross-linked hyaluronic acid (HA) based gel (CMHA-S) to a standard wound management protocol on the healing of acute, full-thickness wounds in dogs. STUDY DESIGN: A prospective, controlled, experimental study. ANIMALS: Purpose-bred, adult, female beagles (n = 10). METHODS: Two 2 × 2 cm wounds were surgically created bilaterally on the trunk of each dog and each side randomized to treatment (CMHA-S) or control (CON) groups. Total and open wound areas were measured with digital image planimetry at 15 time points. From these data, percent contraction and percent epithelialization were calculated. Tissue biopsies were obtained at 6 time points and histologic features were scored. RESULTS: Total wound area was significantly larger and percent contraction was significantly less in CMHA-S compared to CON wounds at all data points between days 9 and 18. At day 25, and for the remainder of the study, CMHA-S wounds were smaller and contracted more than CON wounds, reaching significance at day 32. Percent epithelialization was significantly less in CMHA-S compared to CON wounds at all data points after day 11. Histologically, fibroblastic cellular infiltration was significantly higher in CMHA-S wounds at day 21. CONCLUSIONS: CMHA-S wounds healed more slowly than CON wounds. This HA-based gel is not indicated in acute, full-thickness skin wounds in dogs as administered in this study. However, treatment may be beneficial in the mid-to-late repair stage of healing, or if scar minimization is desired. Further studies to evaluate the effects of the CMHA-S gel on canine wounds are indicated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".