Worldwide interest in silver diamine fluoride over the last decade: A longitudinal retrospective study
Bibliographic record
Abstract
BACKGROUND AND AIM: Silver fluoride solutions have been used successfully over the past 40 years for the prevention and inhibition of dental caries, in both clinical and in vitro studies.This study was performed to investigate worldwide interest in silver diamine fluoride (SDF) over the last decade using Google Trends.METHODS: On January 6, 2020, the term ‘silver diamine fluoride’ was searched for on Google Trends and the relative search volume (RSV) was downloaded. This search was first performed worldwide, and then within the five most-searched countries, i.e., the United States of America (USA), Canada, Egypt, India, and the United Kingdom (UK), from January 2010 to December 2019. Data were subjected to multiple time-series analysis and Kruskal-Wallis test, and autoregressive integrated moving average (ARIMA) forecasting models were generated.RESULTS: The highest RSVs were obtained in the USA (100) and Canada (99). The monthly RSV values were significantly different among countries (P < 0.001). Multiple time-series analysis showed a marked increase in the number of searches including the term ‘silver diamine fluoride’ since 2014.CONCLUSION: There has been an increase in interest regarding SDF among Google users over the last decade. The increase started in 2014, with the highest number of searches for this term being conducted in the USA and Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".