Evaluation of the safety and effectiveness of intradermal administration of QR678 Neo<sup>®</sup>hair growth factor formulation: A phase‐IV, open‐label, single‐arm multi‐ethnicity clinical trial
Bibliographic record
Abstract
BACKGROUND: hair re-growth factor formulation has already proven its effectiveness and safety in treating androgenetic alopecia and female pattern hair loss. AIM: This study aims to evaluate effectiveness and safety of hair re-growth factor formulation at multiple centers for reduction of hair fall and regrowth of hair in androgenetic alopecia and female pattern hair loss. MATERIALS AND METHODS: An open-label, prospective, and interventional study was carried out at multiple centers of different countries. A total of 2428 patients with androgenetic alopecia and female pattern hair loss within the age range of 18-65 years were included in the study. Hair pull test, global photographic assessment, videomicroscopic assessment tests were performed at the beginning, after fourth and eighth sessions, and self-assessment through questionnaire related to satisfaction and side effects was carried out at beginning and at after eight session. RESULTS: Hair pull test was positive in only 12% of the patients after eight sessions. Beneficial results were noted in the global assessment score (mean-6) after fourth session and eighth session (mean-8). Huge improvement was noted in terminal hair count, vellus hair count, and hair density after eighth sessions. Also, with self-assessment score, a higher satisfaction score was noted. On long-term follow-up as well, no side effects were noted. CONCLUSION: is an innovative, efficacious, and safe technique for the treatment of androgenetic alopecia and female pattern hair loss.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".