Forensic medical reporting of non-fatal injuries in criminal cases in the Netherlands: a mixed-methods analysis of regional practices
Bibliographic record
Abstract
Non-fatal physical injuries are common in criminal cases, and their accurate documentation and interpretation are crucial for legal proceedings. In the Netherlands, forensic doctors provide independent injury reports that range from basic injury descriptions to translations of medical information into lay terms and comprehensive expert analysis. However, prior research indicates that these reports are often absent in court cases, despite their recognized importance-particularly in serious crimes and domestic violence cases. The reasons for this limited availability remain largely unclear. This study examined the extent and consistency of forensic medical reporting of non-fatal injuries in adults in the Netherlands, identified regional disparities in forensic medical practices, and explored barriers affecting report availability in criminal cases. A mixed-method approach was used, combining a national survey of forensic medical departments with an analysis of injury reports from 2018 to 2022. Findings reveal substantial regional differences in investigation methods, reporting standards, and the number of reports produced. Variations were linked to the lack of requesting protocols, unclear case definitions for forensic doctor involvement, and capacity constraints. The roles of treating physicians, police, and victims in documenting injuries were also not clearly defined. To ensure equitable access to forensic medical expertise within the criminal justice system, this study recommends national standardization, clearer case prioritization for forensic medical involvement, enhanced collaboration, and enhanced forensic training for treating physicians.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".