Platelet rich plasma therapy- Myth vs Reality
Bibliographic record
Abstract
The transformative journey of Platelet-rich plasma, fueled by discovery of Platelet-Derived Growth Factor (PDGF), underscores its regenerative potential. This comprehensive exploration delves into fundamental characteristics and applications with a focus on its growing prominence in dermatology and aesthetics. An open labelled interventional study was conducted at a tertiary health care centre in Western India to assess the efficacy and safety of PRP in various indications that mainly included patterned hair loss, post traumatic scars, acne scars, melasma and striae distensae. Patients were included in the study based on pre-determined inclusion and exclusion criteria. 6 sessions of PRP were conducted and response to therapy was evaluated using standard objective and subjective scores. The study included 60% female cases with most patients in the age group of 31-40 years (51.2%). For patterned hair loss, the mean change in hair count at end of therapy was significantly greater for baseline to final (P < 0.005). For post-traumatic scars, the mean change in Vancouver scar score at end of 6th sitting was significantly greater for first visit to last visit (P < 0.001). For melasma, the mean change in Modified MASI score at end of treatment was significantly greater for first visit to last visit (P < 0.001). For acne scars and striae distensae, the mean change in objective assessment scores was significantly greater when compared to baseline (p<0.001). Side effects were predominantly early, with 13.8% reporting pain and 10% a burning sensation, while late side effects were minimal at 3.8%. Subjective improvement was reported by 76.25% of cases, ranging from 51% to 75%. The study's results, while acknowledging certain limitations, emphasize PRP's potential efficacy in diverse dermatological conditions. The study underscores the correlation between dermatological conditions and treatment outcomes, highlighting the positive impact of PRP therapy.
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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".