Bibliographic record
Abstract
Postmortem changes are well known for their possible misinterpretation as traumatic lesions which can mislead to suspicion of violent death and therefore to a forensic autopsy request. As far as we know, a systematic review of the prevalence of such a reason for coroner's autopsy request has not been done yet. A retrospective study of 230 forensic autopsies requested by the Coroner's office from 2002 to 2004 in the province of Quebec, Canada, was conducted by the authors. Of the 230 reviewed cases, postmortem artifacts mistaken for traumatic lesions were found in 18 cases. These misinterpretation were based on 5 categories of portmortem changes: purge fluid drainage in 12 cases (66.7%), bluish discoloration by lividity in 5 cases (27.8%), parchment-like drying of the skin in 4 cases (22.2%), bloating from gas formation in 4 cases (22.2%), and skin slippage in 1 case (5.56%). Therefore, postmortem artifacts misinterpretation occurred in 7.83% (95% confidence interval 0.05-0.12) of all requested forensic autopsies and in 35.29% (95% confidence interval 0.23-0.50) of decomposed autopsy cases. This study clearly establishes the high prevalence of postmortem artifacts as main reason for forensic autopsy request. Hence, in a context of forensic pathologist shortage, the improvement of coroner continuous training may reduce the workload.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".