Evaluation of Platelet-Rich Plasma (PRP) Versus Topical Minoxidil (5%) in Combination with Oral Finasteride for the Treatment of Androgenic Alopecia
Bibliographic record
Abstract
Androgenic alopecia was an inherited condition leading to gradual thinning and loss of hair on the crown and frontal scalp. Objective: To compare efficacy of PRP and topical minoxidil (5%) when used in addition to oral finasteride for treating androgenic alopecia. Methods: This quasi experimental study was conducted at Dermatology Department of Akhtar Saeed Medical and Dental College Rawalpindi from March 2023 to August 2023. Total 80 patients of both genders (40 in each group) aged 18 to 65 years diagnosed with androgenic alopecia. Participants were divided into two treatment groups: Group A received monthly PRP injections oral finasteride and 1 mg daily, while Group B applied topical minoxidil (5%) twice daily oral finasteride 1 mg daily. Efficacy was labeled as a statistically significant increase in mean hair density of at least 20 hair/cm² from pre-treatment to post-treatment using a trichometer. All participants had baseline demographic factors and clinical data. Data were analyzed using IBM SPSS 27.0. Results: The comparison of mean hair density between Group A (PRP and Finasteride) and Group B (Minoxidil and Finasteride) showed that Group A had a significantly higher mean hair density compared to Group B (101.6 ± 11.2 hair/cm² versus 87.0 ± 9.0 hair/cm², p < 0.001), indicating that the treatment in Group A was more effective. Conclusions: This study found that platelet-rich plasma (PRP) as an addition to oral finasteride improves hair regrowth, density, and patient satisfaction more than PRP combined with topical minoxidil.
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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 | 0.000 |
| 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) | 0.001 | 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".