The evolution of postmortem investigation: a historical perspective on autopsy's decline and imaging's role in its revival
Bibliographic record
Abstract
Autopsy is generally regarded as the gold standard for cause of death determination, the most accurate contributor to mortality data. Despite this, autopsy rates have substantially declined, and death certificates are more frequently completed by clinicians. Substantial discrepancies between clinician-presumed and autopsy-determined cause of death impact quality control in hospitals, accuracy of mortality data, and, subsequently, the applicability and effectiveness of public health efforts. This problem is compounded by wavering support for the practice of autopsy by accrediting bodies and academic bodies governing pathology specialty training. In forensic settings, critical workforce shortages combined with increased workloads further threaten sustainability of the practice. Postmortem imaging (PMI) can help mitigate these ongoing problems. Postmortem computed tomography can help clarify manner and cause of death in a variety of situations and has undeniable advantages, including cost reduction, the potential to review data, expedient reporting, archived unaltered enduring evidence (available for expert opinion, further review, demonstrative aids, and education), and (when feasible) adherence to cultural and religious objections to autopsy. Integration of radiology and pathology is driving a transformative shift in medicolegal death investigations, enabling innovative approaches that enhance diagnostic accuracy, expedite results, and improve public health outcomes. This synergy addresses declining autopsy rates, the forensic pathologist shortage, and the need for efficient diagnostic tools. By combining advanced imaging techniques with traditional pathology, this collaboration elevates the quality of examinations and advances public health, vital statistics, and compassionate care, positioning radiology and pathology as pivotal partners in shaping the future of death investigations.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.014 |
| 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".