Utilitarian Aspects of Postmortem Computed Tomography
Bibliographic record
Abstract
Computed tomography has been used in clinical medicine for decades, but only recently introduced into the forensic pathology setting. The reasons for the slow adoption of this technology into the autopsy suite are various, including concerns about funding, infrastructural maintenance, training, competency, and scope of utilization. Practical experience in a busy statewide medical examiner department confirmed the utility of this technology as a part of daily practice. The impact of postmortem computed tomography (PMCT) on casework can be stratified into three broad groups: where PMCT 1) supplants invasive autopsy, 2) supplements invasive autopsy, or 3) has limited or no potential for impact on practice. A detailed understanding of the practical uses of this science is important for the practicing forensic pathologist so as to guide decisions about the ways in which PMCT can be implemented within their own institutions and utilized on a daily basis. Dramatic changes in personal and institutional practice trends can be observed once forensic pathologists are comfortable with the evaluation, documentation, and interpretation of PMCT data. Examples of potential paradigm shifts include the performance of only external examination and PMCT instead of invasive autopsy in many cases of motor vehicle fatalities, suicide with violence, and broad categories of death due to natural disease. Over time, the authors believe that the PMCT will become one of the fundamental tools in the forensic pathologist's toolkit.
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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 | 0.092 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".