An oral French maritime pine bark extract improves hair density in menopausal women: A randomized, placebo‐controlled, double blind intervention study
Bibliographic record
Abstract
Abstract Background and Aims Female pattern hair loss affects females of all ages with a trend to increase after menopause. This disorder may have significant psychological impact and lead to anxiety and depression. Objective In a single center, double blind, randomized, placebo‐controlled study, the effects of oral Pycnogenol® intake (3 × 50 mg/day for a total of 6 months) on hair density, scalp microcirculation, and a variety of skin physiological parameters was studied in Han Chinese menopausal women (N = 76) in Shanghai, China. Methods Measurements were taken at the beginning and after 2 and 6 months, respectively. Hair density was determined by digital photographs and further evaluated by Trichoscan software. Transepidermal water loss was measured by a humidity sensor in a closed chamber on the skin surface. Changes in microcirculation were detected as resting flux on the scalp by reflection photoplethysmography. Results Pycnogenol® intake significantly increased hair density by 30% and 23% after 2 and 6 months of treatment, respectively, as detected by Trichoscan® evaluation of digital photographs. Interestingly, photoplethysmography revealed that this beneficial effect was associated with a decrease in resting flux of the scalp skin, which might indicate an improvement of microcirculation. None of these effects were observed in the placebo taking group. In addition, a significant transient decrease of transepidermal water loss was observed in scalp skin under Pycnogenol,® but not placebo treatment. Conclusion Oral intake of Pycnogenol® might have the potential to reduce hair loss in postmenopausal women.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".