The International Association of Dental Traumatology ToothSOS mobile app: A 2‐year report
Bibliographic record
Abstract
BACKGROUND/AIM: The shift in health care and technology calls for innovation through mobile applications as free educational resources for the masses. The International Association of Dental Traumatology (IADT) created ToothSOS, an app (software application for mobile devices) to provide dental trauma information for patients and professionals. The app contains information on the emergency management and prevention of dental injuries, as well as treatment guidelines for dental practitioners. The aim of this study was to assess public utilization of the ToothSOS app in the first 2 years since its launch. METHODS: The ToothSOS app was launched by the IADT in the first week of April 2018. Data regarding the number of downloads and usage of the app in the first 2 years (from April 2018 to May 2020) were collected and analyzed. RESULTS: The total number of ToothSOS downloads over the 2 years was 47 725. The number of downloads peaked in the first month when the app was initially released. Thereafter, the number of downloads decreased to an average of 1423 ± 363 downloads every month. Europe was the territory with the greatest number of downloads followed by the United States and Canada, Asia, Latin America and the Caribbean, and Africa, the Middle East, and India. CONCLUSIONS: Within as short a period as 2 years, the ToothSOS app continues to gain public interest. Further attempts and public campaigns should be made in order to increase the visibility of the app. Dental professionals should encourage patients and communities to use the app in order to increase awareness for the prevention and proper emergency management of traumatic dental injuries.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".