The shifting preferences of patients and physicians in nonsurgical hair loss treatment
Bibliographic record
Abstract
BACKGROUND: There are multiple etiologies for hair thinning and loss, including genetic, hormonal, immune, scaring, and infectious. Hair loss treatment involves both surgical intervention and nonsurgical therapies such as pharmaceuticals, haircare products, vitamins, and low-level laser therapy (LLLT). While pharmaceuticals have been extensively researched, the efficacy of other therapies remains inconclusive. With so many available treatments, consumers often research their options using search engines such as Google and/or seek help from hair restoration physicians. AIMS: To identify and analyze changing trends in international consumer and physician interest in nonsurgical hair loss therapies. METHODS: Worldwide trends in Google searches of hair loss products (2004-2020) were compared with product prescription frequency surveys from members of the International Society of Hair Restoration Surgery (2004-2019, ~29% response rate). RESULTS: Minoxidil and finasteride were the most prescribed hair loss treatments, while "minoxidil" was the most "Googled" term. Generic products were searched more often than their brand counterparts. Nutritionals and haircare prescriptions increased over time. LLLT was also increasingly prescribed, with Internet searches increasing following government regulation announcements. The COVID-19 pandemic initially negatively affected hair loss treatment searches, which have since returned to, and surpassed, pre-pandemic levels. CONCLUSION: Regulations and social media have influence on consumer interest in hair loss products. A weak economy and coronavirus fears may persuade consumers to turn to cheaper hair loss treatment alternatives. Hair restoration specialists need to keep abreast of online trends to communicate effectively with their patients. Patients should be cognizant of the safety and efficacy of hair restoration treatments.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".