A bibliometric analysis of alternative drug therapy options in the treatment of androgenetic alopecia
Bibliographic record
Abstract
Abstract Background Oral finasteride and topical minoxidil formulations are the only FDA‐approved drug therapies for androgenetic alopecia (AGA). Research into dutasteride, topical finasteride, and nontopical minoxidil (low‐dose oral and sublingual) formulations in the treatment of AGA has spiked within recent years. Early findings show that these alternative drug therapies may have similar to improved efficacy and safety profiles relative to the conventional treatment options. Aims Conducting a bibliometric analysis, compare trends in publications on these alternative drug therapies, identify key contributors, evaluate major findings from top‐cited articles, and elucidate gaps in evidence. Methods A search was conducted on the Web of Science database for publications on the use of alternative drug therapies in the treatment of AGA. A total of 95 publications, published between January 2003—March 2024, and their citation metadata were included in the analysis. Results Dutasteride showed the greatest ( n = 37) and longest (20+ years) history of publications, as well as the highest cumulative citations ( n = 914); however, nontopical minoxidil showed a burst in research activity within the last 5 years ( n = 33 publications since 2019). A relatively low number of randomized control trials ( n = 3) for nontopical minoxidil suggests a need for higher‐quality evidence. Conclusions Our analysis reveals major trends, contributors, and gaps in evidence for alternative drug therapies for AGA, which can help inform researchers on their future projects in this growing field of study. There is enthusiasm for exploring off‐label formulations: nontopical forms of minoxidil (oral and sublingual), topical finasteride, and mesotherapy.
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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 | 0.010 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.194 | 0.253 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".