Effectiveness study of the recombinant enzymes pbserum HIGH in the treatment of pathological scars: a pilot study
Bibliographic record
Abstract
Introduction: Pathological scars, despite the achievements of modern medicine, are still a problem. Its prevalencecan reach up to 50% in emergency surgeries. These scars can lead to physical complications, includingimpaired mobility, altered sensation, and discoloration, and may even cause pain. In this study, we explore thepossibilities of using the combined drug of recombinant collagenase and lyase enzymes, and high molecularweight hyaluronic acid (HMWHA) pbserum HIGH in the treatment of pathological scars.Methods: patients of the main group received a course of intra-cicatricial injections of the drug, treatment resultswere assessed clinically, according to the Vancouver Scar Scale (VSS) and Observer Scar Assessment Scale (POSAS)scales, the results were compared morphologically with standard scars treatment methods (biopsies were takenbefore and after treatment).Results: Clinically, patients of the main group received a pronounced positive transformation of scar tissue in 6weeks, statistical processing of data confirms the reliability of changes, morphological studies prove the normotrophic nature of the changes in the scars (including comparison with the control group).Conclusions: Remedy of recombinant collagenase and lyase enzymes in combination with HMWHA pbserum HIGHin the form of the course of intra-cicatricial injections is a safe and effective method of treating pathological scars.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".