Epidemiology Meets Advocacy: Understanding Pediatric Dental Trauma and Delayed Care in Post-Conflict Syria
Bibliographic record
Abstract
Objective. To evaluate the prevalence, risk factors, aetiology, and management of traumatic dental injuries (TDIs) among children aged 1–18 years attending the Department of Pediatric Dentistry, Damascus University, Syria, during 2023–2024, and to illustrate representative clinical cases with documented outcomes. Methods. This retrospective cross-sectional study reviewed 2716 patient records (2023–2024) and identified 301 children with TDIs. Demographic, clinical, and behavioural variables were extracted and analysed using χ2, t tests, ANOVA, and binary logistic regression (IBM SPSS v26). Results. The overall TDI prevalence was 11.08%. Males were over twice as likely as females to experience TDIs (OR = 2.30; 95% CI = 1.76–3.01; p < 0.001). Older age acted as a protective factor (OR = 0.56; 95% CI = 0.43–0.74; p < 0.001). Falls were the most common cause (63.7%), and injuries most often occurred at home (48.9%). The maxillary central incisors were most frequently affected (68.5% of cases). Children with special healthcare needs had significantly more traumatised teeth (mean = 2.61 ± 1.13) than healthy children (1.66 ± 0.92; p < 0.001). Nearly half of the patients (45.3%) presented > one month after injury, and asymptomatic apical periodontitis and reversible pulpitis were the most frequent diagnoses. Representative case presentations demonstrated multidisciplinary management using restorative, endodontic, and orthodontic approaches with favourable follow-up outcomes. Conclusions. TDIs affected about one in nine children in this Syrian cohort. Male gender, younger age, and previous trauma were key risk factors. The predominance of delayed presentation underscores the need for community education, early referral systems, and targeted preventive programs within school and home environments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".