Patterns of Public Interest in Lipomas and Lipoma-Removal Procedures: Google Trends Analysis
Bibliographic record
Abstract
Background: Lipomas are benign tumors composed of encapsulated adipocytes. Although relatively common, uncertainty remains about the population-level prevalence, the etiology, and the degree of public interest in lipomas and associated removal procedures. Objective: The spatiotemporal patterns of public interest in lipomas and lipoma removal procedures were characterized. Methods: Google Trends data that report the relative search volume (RSV) of Google queries pertaining to lipomas and their removal procedures at national and international levels were analyzed. To contextualize these trends, the RSV for lipomas was compared to that of several other common dermatological conditions in the United States. Results: In the United States, lipomas have consistently generated lower levels of public interest than other common dermatological conditions, but interest in the condition has been rising since the mid-2010s. Across the world, public interest in lipomas appears to be the highest in pockets of Eastern Europe, whereas in the United States, relative interest has been higher in Midwestern and Southern states. In addition, the interest in lipoma removal procedures has risen steadily from 2004 to the present, with particularly high RSVs coming from Southwestern states. Conclusions: Dermatologists and plastic surgeons should be aware of the increasing public interest in lipomas and lipoma-removal procedures. Clinical awareness is especially important in states with an elevated interest in lipomas and their associated removal procedures.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".