Platelet Rich Plasma: Is it Effective in Treatment of Atrophic Scar?
Bibliographic record
Abstract
Introduction: Atrophic scar is the result of decreasecollagen production and matrix formation, that's why managementof atrophic scar is challenging for plastic surgeons.PRP plays an important role in tissue regeneration duringwound healing via release of growth factors that have animportant role in the regulation and proliferation of mesenchymaland fibroblast cells, hence it increase collagen productionand improve wound healing. The main goal of thearticle is to evaluate the efficacy and safety of autologousplatelet-rich plasma (PRP) injections in improvement ofatrophic scar.Patients and Methods: Twenty patients with atrophic scarwere included in this study at plastic surgery department,Assiut university hospital, in duration from July 2016 toFebruary 2017. Mean age was 22.68±6.75 years. Patientswere randomly divided into 2 groups to allow equal distribution.Group 1 (control), underwent scar revision only. Group2 (study), underwent scar revision, followed by immediatelyintradermal autologous Platelet rich plasma (PRP) injectionat the edges of the wound. This was followed by PRP injectionevery month for the next five months. Patients with atrophicnon pigmented scars at any region of the body were included.Any case with history of medical co-morbidities also wasexcluded. Scar width, Vancouver scar scale (VSS), surgeonassessment scar scale, patient assessment scar scale andcomplications, were the outcome measurements.Results: Surgeon assessment scar scale, VSS, and patientassessment scar scale showed no significant difference (pvalue<0.05) between the two groups preoperative, whilethere was significant clinical improvement of the resultingscar in group II compared to group I six months postoperatively,No complications were noticed in both groups.Conclusion: We conclude that intradermal injection ofautologous PRP in an atrophic scar after its revision could beconsidered as a promising option for atrophic scar managementas it improves wound healing process that appears in theimprovement of clinical appearance of the scar.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".