Comparison of Injury Patterns in Consensual and Nonconsensual Sex: Is It Possible to Determine if Consent was Given?
Bibliographic record
Abstract
Matters of sexual consent and sexual assault are hotly debated issues among professionals and laypersons alike. A widespread misconception of sexual assault is that most victims of sexual assault sustain significant physical trauma. It is the purpose of this review article to compare the patterns of physical injury (both genital and extragenital) in victims of sexual assault and participants of consensual sex to conclude if physical injury alone can indicate whether consent was given. Interpretations of injury have great forensic significance as it can influence the outcome of sexual assault cases. Several articles indicate that extragenital injuries are commonly found in sexual assault victims (46%-82%) and that most of such injuries are deemed minor. Articles report a wide range of genital injury detection rates in both sexual assault victims (6%-87%) and consensual sex participants (6%-73%). Usage of different examination techniques may partly explain the wide range of detection rates reported. Out of all those who sustained genital injuries, only a small portion of people required hospitalization. In both consensual and sexual assault cases, genital injuries in the 6 o'clock position were most common. Studies of genital injury lacked standardization of factors that significantly influence the results, such as time to examination after sex, examination techniques, and injury severity scales. Therefore, medicolegal personnel should be aware that sexual assault victims can present with a wide range of physical trauma and should avoid relying on physical trauma alone to conclude whether consent was present.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".