Evaluation of linear scars revision with and without platelet-rich fibrin and autologous unprocessed bone marrow injection
Bibliographic record
Abstract
Introduction: There are many modalities of traditional methods of linear scar treatment including derma abrasion,chemical ablation, laser therapy, and use of surgical excision and grafting. However, surgical treatments, with or withoutsupplementary nonsurgical treatments offers a confusing picture of widely variable ‘success’ rates, recurrence rates,patient populations, and follow-up periods.Aim: Our main objective was to improve the results of treatment of linear scars. Also to evaluate of the results of addingplatelet-rich fibrin (PRF) and autologous unprocessed bone marrow in surgically revised linear scars.Patients and Methods: Total study number of 16 patients (nine men, seven women), aged 22–52 years were enrolled inthis study. Six patients had the scar in the abdomen, four patients had the scar in the forearm, three in the leg and threein the neck. Assessment of scar was done including history, clinical examination using Vancouver Scar Scale, patient’and doctor’ scar satisfaction. All patients were treated with scar revision by injecting half of the wound with the aspiratedautologous unprocessed bone marrow and PRF. On follow-up, the patients were photographed at the start of the study(preoperative), weekly for first 2 weeks (postoperative), and monthly for the next 6 months.Results: Adding heparinized autologous unprocessed bone marrow and PRF; may improve the pattern of scar revision.This preliminary work suggests that there were differences in the time of healing, scar appearance (100%) as P value was0.021, pliability, height (62.5%) a P value was (0.009), vascularity and satisfaction (43.8%) between both groups of thestudy.Conclusion: This novel treatment appeared to be safe and effective for scar treatment. To illustrate significant statisticaldifferences, we need a larger sampling and longer follow-up periods.
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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) | 0.002 | 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".