Are invasive postmortem examinations still the ‘gold standard’ in diagnosing the cause of death?
Bibliographic record
Abstract
For many years, the foundations of both its art and science in medicine grew from the observations made at the autopsy table. The observations of the pathologists formed the pathologic basis and manifestations of disease, which further advanced the development of therapeutics. For most of the 18th and 19th centuries, morbid anatomy (autopsy pathology) was considered the science of medicine. Today, the autopsy and the knowledge that derives from it do not hold the position they once did in the profession’s history. There is a belief among the medical community a complete autopsy is necessary to find out the answers when someone dies. In some jurisdictions, the forensic pathology community is reluctant to follow the targeted or minimally invasive approach in postmortem examinations due to a lack of understanding of the process. With a complete external examination, trace evidence collection, total body computed tomography (CT) scan, and minimally invasive/targeted dissection with a collection of samples for further analysis, almost all answers can be provided in hospital and medicolegal autopsy settings. We must collaborate with sister disciplines, such as experts in radiology and the legal community, to educate them on the importance of this new approach using advanced technology.Continuously evolving modern radiological imaging has remarkably increased the accuracy of clinical diagnosis. Conventionally, when you hear the word postmortem examination, people think it includes an external examination and dissection of all body cavities. In the 21st century, most questions raised by the family, clinicians, the coroner (medical examiner), courts, and law enforcement agencies can be answered with a complete external examination of the body, total body CT scan +/- magnetic resonance imaging, and targeted dissection or minimally invasive approach incorporated with sample collection for further testing. This is cost-effective and can produce reviewable data.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".