Trends and seasonality in public interest in dental trauma: Insights from Google Trends
Bibliographic record
Abstract
AIM: To assess trends and seasonal variation of dental trauma by using the Google Trends data. DESIGN: Google Trends was used to obtain relative search volume (RSV) of search terms such as dental trauma, broken tooth, chipped tooth, knocked-out tooth, avulsed tooth, and gum trauma. The search strategy was set to the time period (January 2004 to December 2019), region (worldwide, the United States, the UK, Australia, Canada, New Zealand, Ireland, and Turkey), Web search, and all categories. Seasonal variation was evaluated using the cosinor analysis. RESULTS: The worldwide RSV values of broken tooth, chipped tooth, knocked-out tooth, and avulsed tooth have shown a general increase in recent years with an upward forecast line. The RSV values of dental trauma have shown a general increase in recent years with a plateau forecast line, and gum trauma has shown a stable trend with a plateau forecast line. Seasonal variation of chipped tooth, broken tooth, dental trauma, knocked-out tooth, avulsed tooth, and gum trauma was not found statistically significant in any of the countries (p > .025). The top related queries of chipped tooth and broken tooth were about pain, fix/repair, and cost. The top related topics for avulsed tooth and knocked-out tooth are about infant, child, toddler, and primary tooth. CONCLUSIONS: People's interest on dental trauma, broken tooth, chipped tooth, knocked-out tooth, and avulsed tooth has shown a general increase in recent years without showing a seasonal pattern. Healthcare professionals should pay more attention to people's concerns and informational needs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.030 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".