The prevalence of traumatic dental injuries in primary teeth: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background/Aims The varied prevalence of traumatic dental injuries (TDI) in primary teeth around the globe raises a serious knowledge gap in the available literature. The aim of this study was to evaluate the prevalence of TDI in primary teeth and also to evaluate the different factors associated with TDI in primary teeth. Materials and Methods Comprehensive searches were performed in PubMed, Embase, Google Scholar, and The Cochrane Central Register of Controlled Trials with predefined search criteria. The primary outcome was the prevalence of TDI in primary teeth, and the secondary outcomes were the factors associated with TDI in primary teeth. Qualitative analysis was done using the Newcastle‐Ottawa scale adapted for cross‐sectional studies. The random‐effect model was used for meta‐analysis, and meta‐regression analysis was done to evaluate the heterogeneity between the included studies. Meta‐analysis was done using the “meta” package of “R” language. The overall quality of evidence was assessed using GRADEpro GDT software. Results A total of 24 cross‐sectional studies met the inclusion criteria representing 4876 TDIs in 22 839 children aged between 0 and 6 years old. The overall prevalence of TDI in primary teeth was 24.2% (95% CI: 18.24‐31.43, P = 0, I 2 = 99%). Falls contributed the highest number of TDI ‐ 59.3% (95% CI: 41.05‐76.40, P < .01, I 2 = 98%) ‐ in primary teeth. The most common type of tooth fracture in primary teeth was an enamel fracture (61.9%), and prevalence of TDI in children with incompetent lip closure was 49.4%. Conclusion The prevalence of TDI in cross‐sectional studies of primary teeth was 24.2% with very low quality of evidence. Falls contributed the highest number of TDI in primary teeth, accounting for 59.3%. Children with incompetent lip closure have the highest prevalence (49.4%) of TDI in primary teeth.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".