Prevalence of dental, oral, and maxillofacial traumatic injuries among domestic violence victims: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND/AIMS: Domestic violence (DV) encompasses a series of abusive behaviors, perpetrated in a family environment, against individuals of all ages and genders. Injuries to the head, neck, and face are frequent findings among victims of abuse, negatively impacting their quality of life. Although oral and maxillofacial injuries (OMFI) and traumatic dental injuries (TDI) are commonly diagnosed among DV victims, their prevalence is still unknown. This systematic review was aimed to assess the prevalence of OMFI and TDI among victims of DV. METHODS: The protocol of the review was registered in PROSPERO (CRD42023424235). Literature searches were performed in eight electronic databases, up to August 7th, 2023. Observational studies published in the Latin-roman alphabet and reporting the prevalence of OMFI and/or TDI were included. The Joanna Briggs Institute's critical appraisal tool, checklist for prevalence studies, was used for quality assessment. Results were presented as qualitative and quantitative syntheses. RESULTS: = 99%) was demonstrated in samples with only women. OMFI was less prevalent (20%) among DV victims under 18, while TDI was lower among adults (1%). Hospital samples presented higher pooled prevalence of OMFI (32%), and forensic data from fatal victims presented higher prevalence of TDI (8%). CONCLUSION: The overall prevalence of OMFI and TDI in DV victims was 29% and 4%, respectively. Women victims of DV presented higher rates of OFMI (41%) and TDI (6%).
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| 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 | 0.001 |
| 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".