Prevalence of Adult Female Genital Trauma After Acute Sexual Assault
Bibliographic record
Abstract
BACKGROUND: Examination of the adult female genitalia after an acute sexual assault may reveal findings interpreted by the examiner as injuries, which may be linked to later legal outcomes. There is no consistent definition in the literature regarding what findings constitute genital trauma after sexual assault. We studied how the prevalence of genital trauma is impacted by the inclusion/exclusion of various genital findings reported in the literature. METHODS: A retrospective descriptive chart review of the sexual assault forensic records from a provincial regional sexual assault treatment center was conducted over a 4-year period and included 67 female patients, 12 years old and over, who reported being sexually assaulted in the previous 72 hours and received a complete forensic examination. We studied the prevalence of genital trauma, using eight definitions of trauma, as well as the percentage of each type of genital finding within this population. RESULTS: The prevalence of genital trauma in this population ranged from 52%, the majority, to 31% of women, depending on the definition of trauma utilized. Forty-one percent of the findings, the greatest number overall, were redness. Bruises, abrasions, and tears (lacerations), the components of blunt force trauma, accounted for 4%, 15%, and 14% of the findings, respectively. INTERPRETATION: A universal definition of what findings constitute genital trauma after acute sexual assault is required if the examiner, as expert witness, is to compare findings in a given case with the broader literature and assist the court in ensuring an informed process of decision making.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".