Global and national public awareness and interest in glomerular diseases from 2004 to 2024
Bibliographic record
Abstract
Background: Glomerular diseases significantly impact global health. This study investigated public interest in five common glomerular diseases. Methods: Google Trends™ were used to analyze search activity from January 2004 to December 2024 for IgA nephropathy (IgAN), membranous glomerulonephritis (MN), focal segmental glomerulosclerosis (FSGS), lupus nephritis (LN), and diabetic nephropathy (DN). Data were retrieved both globally and in English-speaking countries, including the United States. Monthly and yearly relative search activity were assessed and compared. Results: Globally, IgAN had the highest average relative search activity, followed by DN, FSGS, LN, and MN. Both IgAN and FSGS exhibited declining trends, while LN showed an upward pattern. MN and DN experienced a modest decline before 2016, preceded by a slight increase. Among English-speaking countries, search interest was predominantly concentrated in five countries, primarily including the United States, United Kingdom, Canada, and Australia, with the United States consistently ranking as the leading country. For IgAN, LN, and MN, the trends observed in the United States appeared to align with global data. In contrast, search interest for FSGS exceeded global levels, while interest in DN was slightly lower than global activity. In the United States, IgAN, FSGS, and LN were most prominent in North Dakota, Massachusetts, and Delaware, respectively, while DN and MN saw peak activity in West Virginia. Conclusion: Public engagement with glomerular diseases has not uniformly grown, at least in English-speaking countries, emphasizing the need for enhanced awareness efforts. Future analysis should prioritize search terms in the predominant language of each country.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".