Google search trends in onychomycosis: Influences of flip flops and advertising
Bibliographic record
Abstract
BACKGROUND: Onychomycosis is a fungal infection of the nail, affecting 5.5% of the population. Individuals affected by this disease experience increased anxiety about this disorder and a decreased quality of life. There are multiple available treatments across the globe, leading people to search online for information on the various therapies. AIMS: To analyze Google search trends of fungal infection and treatment keywords and the influence of different geographical locations, season, regulatory decisions, and advertisements on these trends. METHODS: In May, 2020 we used Google Trends to determine the relative interest of various fungal infection and treatment keywords worldwide and in the US, the UK, Canada, and Australia. Notable peaks were investigated for contemporaneous news events. RESULTS: In general, searches for toenail fungus and associated treatment terms peak during the summer months. Interest in individual treatments peaks when a product is launched, is the subject of an advertising campaign, or becomes more available to the public through approval or reclassification. Yeast infection, thrush, and ringworm terms are consistently searched more often than toenail fungus, jock itch, or foot fungus; all terms are most popular in the summer months, with toenail fungus reaching annual popularity one month prior to jock itch. DISCUSSION: In general, interest in toenail fungus and treatments is the highest when social anxiety about toenail appearance and the occurrence of fungal infection is the greatest. Curiosity about treatment products increases with their availability and visibility to the public. Combining the power of seasonal interest and advertising generates the greatest search profile for onychomycosis treatments.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".