Analysis of Keywords Used in Internet Searches for Melanoma Information: Observational Study
Bibliographic record
Abstract
Background: The internet is an accessible resource for health care information and is often used by patients to learn about melanoma. The keywords that are used in internet searches can reflect internet users' interest in specific topics and the public's awareness of health-related issues. Objective: This study aims to describe the most frequently used keywords, questions, and corresponding websites in internet searches for melanoma. Methods: This is an observational study using data retrieved from Google Trends, Alexa Internet, SEMrush, Ahrefs, and SE Ranking for the keywords "melanoma" and "skin cancer." Results: Average search interest as per Google Trends was greater for the keyword "skin cancer" than for the keyword "melanoma." Searches for the top 25 keywords in 3 databases resulted in 34 unique melanoma keywords and 33 unique skin cancer keywords. Melanoma keywords were most frequently related to clinicopathologic classification (n=11, 32%), and skin cancer keywords were most frequently about diagnosis (n=14, 42%). Questions about the prognosis of melanoma appeared most frequently among the most popular melanoma questions, but general questions or questions about the diagnosis of melanoma contributed the greatest proportion of searches by search volume. Skin cancer question searches were most commonly about diagnosis. The highest proportion of searches for popular melanoma and skin cancer keywords most frequently sent traffic to websites from nonprofit organizations and media companies, respectively. Conclusions: We identified common keywords, questions, and websites used to access information about melanoma on the internet. These data may help health care providers and public health professionals when educating and counseling patients and the public about skin cancer.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".