Local injection of platelet-rich plasma is effective for non-healing hand wounds
Bibliographic record
Abstract
OBJECTIVE: To evaluate the clinical efficacy of local platelet-rich plasma (PRP) injections in treating non-healing hand wounds. METHODS: Data of 80 patients with non-healing hand wounds were retrospectively selected for this study. Among them, 48 patients in the research group received local PRP injections, while 32 patients in the control group were treated with conventional dressing changes. The outcomes assessed included treatment efficacy, safety, frequency of dressing change, wound healing time, duration of hospitalization, treatment costs, wound healing rate, wound infection rate, Vancouver Scar Scale (VSS) scores, Bates-Jensen Wound Assessment Tool (BWAT) scores, Visual Analogue Scale (VAS) scores, serum wound growth factors, and patient satisfaction with wound appearance. RESULTS: The research group demonstrated significantly superior outcomes compared to the control group, including higher overall treatment efficacy and wound healing rates. Furthermore, the research group exhibited a significantly lower incidence of adverse events, reduced frequency of dressing changes, shorter wound healing time, reduced hospitalization duration, lower treatment costs, and a lower infection rate. Post-treatment assessments revealed significantly lower VSS, BWAT, and VAS scores in the research group. Additionally, the upregulation of serum wound growth factors was more pronounced in the research group following treatment. CONCLUSIONS: Local PRP injection demonstrates clear efficacy in the management of non-healing hand wounds.
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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 | 0.002 |
| 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) | 0.002 | 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".