Hair loss treatment and restoration: Online interest trends and video‐based social media content
Bibliographic record
Abstract
BACKGROUND: Public interest in hair loss topics and treatments can be observed by examining online trends, such as those monitored by Google Trends. Social media has also gained the interest of the dermatology community. Video-based social media sites are being used to provide the public with informational content related to hair loss and treatments, but it may not always be unbiased or reliable. AIMS: This research examined the interest in hair loss and treatment related trends in recent years through Google Trends, as well as examined what videos from YouTube and TikTok that the average person may encounter when performing searches related to hair loss and treatments. RESULTS: Google Trends showed that the COVID-19 pandemic had an initial negative impact on interests of hair loss and treatment related topics, both worldwide and in the United States. External events, such as a celebrity receiving a hair transplant, can influence the interests of the general public with the topic. The social media sites showed that there was a high level of interest in the topics, approximately 25% of videos involved a medical professional, and many involved personal experiences or natural remedies. CONCLUSIONS: Interest in hair loss and treatments continues to grow. Clinicians should do their best to follow the current public interests and be aware of where patients may be obtaining information. Being aware of general interest trends online can benefit clinicians by allowing them to prepare their clinics for potential influxes.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".