Role of Platelet-Rich Plasma injection during Modified Radical Mastectomy
Bibliographic record
Abstract
Background: platelet-rich plasma (PRP) is a human plasma product enriched by platelets, growth factors, and fibrinogen with high hemostatic and healing properties. Objectives: The aim of this study was to evaluate the effect of autologous PRP on wound healing in women undergoing modified radical mastectomy (MRM). Patients and Methods: In this randomized and controlled trial, 100 females were admitted to El Sayed Galal hospital, Egypt from October of 2017 to August of 2018 for MRM. The patients were randomly assigned to two groups. The intervention group received PRP after surgery, whereas the control group received the usual care. All patients were evaluated at baseline, five days, and 2 weeks after surgery. The primary endpoint used the REEDA scale for assessing the changes in wound healing. The secondary outcome measures used were the Vancouver scar scale (VSS). All scale scores were analyzed using a repeated measures test for the variance. Results: At the end of the study, the PRP group showed a greater reduction in the edema ecchymosed discharge approximation (REEDA) score compared to the control group (85.5% reduction in the PRP group; 72% in the control group) (P < 0.001). Furthermore, patients treated with PRP experienced a 93% reduction in the VAS score at the end of follow-up, but the control group only observed a 79% reduction (P < 0.001). Conclusions: It seems that applying PRP is an effective therapeutic approach for wound healing, and faster wound healing is expected due to the presence of more platelets and growth factors.
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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 | 0.001 | 0.001 |
| 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".