A cannabinoid <scp>Hairy‐Tale</scp>: Hair loss or hair gain?
Bibliographic record
Abstract
BACKGROUND: Few studies have reported on the use of cannabinoid products to treat hair loss. AIM: This article aims to reconcile cannabinoids' impact on hair growth. METHOD: A comprehensive and structured search was conducted in PubMed and Google Scholar on June 23, 2022. RESULT: While cannabidiol (CBD), a phytocannabinoid, may cause hair growth, several other phytocannabinoids may lead to hair loss. Additionally, the effect of CBD on hair growth may be concentration-dependent. CBD may cause hair loss at high concentrations (≥10 μM). Therefore, the concentration of CBD needs to be adjusted so that it is optimal for hair growth. One trial found that once-daily application of CBD-rich topical cannabis extract for 6 months increased nonvellus hair count by approximately 93.5% in 35 Caucasian AGA patients: 28 males aged 28-72 years [average 43 years] and 7 females aged 46-76 years [average 61 years]. Each application contained 3-4 mg of CBD. The CBD-rich topical cannabis extract was prepared by ultra-pulverizing Cannabis sativa [hemp] flower into a green chalk-like powder [10.78% CBD and 0.21% tetrahydrocannabinol] and then infusing the powder into a lanolin paste and Emu oil carrier. CONCLUSION: Topical CBD preparations require further studies to establish their safety and efficacy profile. An ideal topical cannabinoid preparation should contain CBD at the right concentration and lack other phytocannabinoid adulterants.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".