The efficacy and safety of platelet-rich plasma in the tendon-exposed wounds: a preliminary study
Bibliographic record
Abstract
Abstract Objective Currently, among wounds with large skin tissue defects caused by various reasons, the treatment of refractory wounds is still a major clinical problem. This study is aimed to preliminarily assess the therapeutic potentials of platelet-rich plasma (PRP) in refractory wounds with exposed tendons, as well as corresponding efficacy and safety. Methods A total of 12 patients (5 males and 7 females) with refractory wounds and exposed tendons who were admitted to our hospital from June 2018 to December 2020 were included in this study. After the preparation of PRP, the included patients underwent the PRP injection after the debridement of wounds, and the efficacy and prognosis were assessed by the same group of senior surgeons. Results The average age of included patients was 42.7 ± 12.9 years, and the causes of injury included traffic accidents (3 cases), contusion (2 cases), burns (2 cases), diabetes complications (4 cases), and melanoma complications (1 cases). The average healing time was 23.0 ± 5.0 days, and the mean size of the wound was 3.1 × 5.1 cm2. During the whole treatment process, Vancouver Scar Scale (VSS) decreased from 7.4 ± 1.6 before PRP treatment to 3.6 ± 0.9 after treatment (P < 0.001), Manchester Scar Scale (MSS) decreased from 12.3 ± 4.5 before PRP treatment to 5.4 ± 1.2 after treatment (P < 0.001), and no redness and swelling were observed around wounds, the size and degree of wounds gradually reduced, the coverage rate of granulation tissue was acceptable, overall quality of scar was relatively good, skin sensitivity around wounds was normal, there was no local wounds secretion, and postoperative patient's satisfaction was relatively good during follow-up. Conclusions Our study has preliminarily indicated that PRP can promote the wounds healing, reduce the inflammation around wounds, and improve the granulation tissue and angiogenesis, thereby effectively polishing up the safety and efficacy.
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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) | 0.000 | 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".