Dental and maxillofacial injuries associated with domestic violence against women in Israel: A report for 2011–2021
Bibliographic record
Abstract
BACKGROUND/AIM: The WHO, in its 2002 report, indicated the dramatic worldwide increase in the incidence of intentional injuries affecting people of all ages and both sexes, but especially children, women, and the elderly. The aim of this study was to analyze dental and maxillofacial injuries associated with domestic violence against women in Israel between the years 2011-2021. METHODS: This was a retrospective cohort study based on data from the Israeli National Trauma Registry (INTR). The INTR provides comprehensive data on hospitalized patients from all six Level I trauma centers (TC) and 15 of the 20 Level II TCs in Israel. Women, ages 14 and older, injured and hospitalized due to domestic violence between 2011 and 2021 were identified. RESULTS: Between 2011 and 2021, there were 1818 cases of women ages 14 + that were hospitalized due to violence, excluding terror, occupational trauma, and attempted suicide. Out of these injuries, 753 cases were attributed to domestic violence, 537 were defined as non-domestic violence and 528 were a result of a brawl/fight. Of the domestic violence cases, 5% (38) exhibited maxillofacial injuries compared to the non-domestic violence cases where 6.2% (33) exhibited maxillofacial injuries and the brawl group where 5.7% (30) exhibited maxillofacial injuries. The most injured areas in domestic violence cases were the maxilla followed by the zygomatic bone and the mandible. Almost half of the domestic violence cases (47.7%) required surgical intervention during their hospitalization. The spouse was the perpetrator responsible for the domestic violence in the majority of the cases. CONCLUSIONS: Dental professionals might be able, in some cases, to identify and report domestic violence signs and thus, better understanding of the specific characteristics of domestic violence related to traumatic injuries is important.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".