Do Non-Prescription Products Help in Managing Androgenic Alopecia?
Bibliographic record
Abstract
Background: Androgenetic alopecia (AGA) is the most common cause of hair loss. Currently, approved medications for AGA are topical minoxidil and oral finasteride, both of which are prescription medications which may cause side effects. Non-prescription products such as herbal extracts and over-the-counter medications have limited evidence regarding safety and efficacy; however, they may be an alternative for patients unable or unwilling to use prescription medication. Summary: bran, pumpkin seed oil, rosemary oil, saw palmetto, and watercress. The available data demonstrate considerable improvements in one or more parameters: total hair density, terminal hair density and hair diameter. Procyanidin and cetirizine were investigated in more investigator-blinded, randomized controlled trials than other agents. Minimal adverse events were observed; however, more robust clinical trial and long-term safety and efficacy data are warranted. Key Message: Additional investigations through the conduct of high quality randomized, controlled trials with larger numbers of patients will help determine the effectiveness and safety of this class of compounds, either as monotherapy or as an addition to current pharmacological interventions.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".