Eczema, Atopic Dermatitis, or Atopic Eczema: Analysis of Global Search Engine Trends
Bibliographic record
Abstract
BACKGROUND: The lack of standardized nomenclature for atopic dermatitis (AD) creates challenges for scientific communication, patient education, and advocacy. OBJECTIVE: We sought to determine the relative popularity of the terms eczema, AD, and atopic eczema (AE) using global search engine volumes. METHODS: A retrospective analysis of average monthly search volumes from 2014 to 2016 of Google, Bing/Yahoo, and Baidu was performed for eczema, AD, and AE in English and 37 other languages. Google Trends was used to determine the relative search popularity of each term from 2006 to 2016 in English and the top foreign languages, German, Turkish, Russian, and Japanese. RESULTS: Overall, eczema accounted for 1.5 million monthly searches (84%) compared with 247 000 searches for AD (14%) and 44 000 searches for AE (2%). For English language, eczema accounted for 93% of searches compared with 6% for AD and 1% for AE. Search popularity for eczema increased from 2006 to 2016 but remained stable for AD and AE. CONCLUSIONS: Given the ambiguity of the term eczema, we recommend the universal use of the next most popular term, AD.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".