A meta-analysis to evaluate the prevalence of maxillofacial trauma caused by various etiologies among children and adolescents
Bibliographic record
Abstract
Aims: Children and adolescents who are affected by trauma may have complications that are more serious and dangerous. Herein, a meta-analysis to evaluate the prevalence of maxillofacial trauma caused by various etiologies according to the geographic regions of the world among children and adolescents was conducted. Materials and Methods: A comprehensive search was performed in four databases of PubMed/MEDLINE, Web of Science, Cochrane Library, and Scopus from January 1, 2006 until July 7, 2021. To evaluate the quality of included articles, an adapted version of the Newcastle-Ottawa scale was used. The prevalence of maxillofacial trauma was estimated by event rates and 95% confidence intervals in relation to etiology and geographic region of study population. Results: Through search in the databases and the electronic sources, 3071 records were identified, and 58 studies were eligible for inclusion in the meta-analysis. A total of 264,433 maxillofacial trauma cases were reported by all included studies. Globally, the overall prevalence of maxillofacial trauma was highest due to Road Traffic Crashes (RTC) (33.8%) followed by falls (20.7%), violence (9.9%), and sports (8.1%) in children/adolescents. The highest prevalence of maxillofacial trauma were observed in African population (48.3%) while trauma due to falls was most prevalent in Asian population (44.1%). Maxillofacial trauma due to violence (27.6%) and sports (13.3%) were highest in North Americans. Conclusion: The findings demonstrate that RTC was the most prevalent etiology of maxillofacial trauma in the world. The prevalent causes of maxillofacial trauma differed between the regions of study population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".