Dental injuries at the Xi’an, China Stomatological Hospital: A Retrospective Study
Bibliographic record
Abstract
BACKGROUND/AIM: In order to enrich epidemiological knowledge regarding traumatic dental injuries (TDI) in China, and to further improve the treatment, prevention and education of TDI, the aim of this study was to retrospectively analyze the TDI that presented to the emergency dental department at the Stomatological Hospital in Xi'an, China. METHODS: This retrospective study included all first-visit patients who presented with TDI at the Stomatological Hospital affiliated with the Fourth Military Medical University in Xi'an, China, between January 2013 and June 2019. Data were extracted using the terms of diagnosis of TDI from the hospital database. RESULTS: Overall, 965 (606 males and 359 females) files were reviewed. The average age was 22.8 ± 13.4 years. Among the 2059 teeth injured (average of 2.1 teeth per patient), the maxillary incisors (1751; 85.0%) were the most prevalent teeth to present with injuries, while the main types of injuries were concussions (14.8%) enamel-dentin-fractures (14.50%) and enamel-dentin-pulp fractures (14.0%). After initial examination and diagnosis, 4.2% patients refused treatment. CONCLUSIONS: The epidemiological statistics of TDI in Xi'an, China show consistency with other studies from around the world, but they also vary in diagnosis proportion and the choice of treatments. This information may further instruct treatment, prevention and emergency resources distribution to target the high-risk groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".