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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".