Changing trends in surgical hair restoration: Use of Google Trends and the ISHRS practice census survey
Bibliographic record
Abstract
BACKGROUND: Hair loss affects most people at some point in their lifetime, causing anxiety and decreased self-esteem. There are multiple surgical and nonsurgical treatments available, with the surgical options having greater and longer-lasting effects. Such treatments have evolved over time with advances in technology and research, with numerous patients researching these treatments on Google. Many surgeons who provide these treatments belong to the International Society of Hair Restoration Surgeons (ISHRS). AIMS: To investigate trends in surgical hair restoration treatment from both the surgeon and patient perspectives. METHODS: Patient epidemiological and surgical data from the ISHRS were combined with search trend data from Google to analyze changing trends in surgical hair restoration treatment. RESULTS: Worldwide Internet searches for "hair transplant" have increased from 2004 to the present. Follicular unit excision (FUE) has supplanted follicular unit transplant (FUT) as the most popular hair transplant performed. Since 2004, there has been an increase in both nonsurgical and surgical female patients. Beard and eyebrow transplants have increased in popularity. Google searches follow this trend. Nonsurgical treatments such as platelet-rich plasma (PRP) are being searched more frequently. Hair restoration clinics and Google searches were affected adversely by the COVID-19 pandemic. CONCLUSION: Technological advances in available therapies, improvement in delivery systems, changes in hair fashion, and global events have direct impact on hair restoration treatments offered by physicians and researched by patients. It is in the best interest of all hair restoration providers to keep abreast of changing technologies and treatment trends to stay at the forefront of their profession.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".