Postmortem CT in Trauma: An Overview
Bibliographic record
Abstract
As forensic radiology sees an exponential gain in popularity, postmortem computed tomography (PMCT) is increasingly being used in the appropriate setting, either as preautopsy guidance or as part of complementary virtual autopsy protocol. Many articles have expounded the value it adds to forensic pathology in the general setting and the appropriate technical parameters to be used for optimum benefit. We aim to put forth a concise review on the role of PMCT specifically in trauma and the pitfalls to be aware of. Reviews have shown that presumed cause of death in trauma have been proven by autopsy to be wrong in about 30% cases. Radiology applied to postmortem investigation in unnatural deaths and more specifically in trauma shares many semiotic features with emergency radiology. Therefore, in the near future, emergency radiologists might be required to integrate this type of imaging in their regular practice. Although the predominant drawbacks are time-dependent, PMCT also has some difficulty in differentiating antemortem and postmortem events. However, in many such scenarios, PMCT and autopsy play a complementary role in arriving at conclusions, and we believe understanding the benefits and role in trauma is imperative considering the expanding usage of PMCT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".