The Value of Molecular Autopsy: Genetic Testing Reveals Long QT Mutations in an Autopsy-Negative, Postpartum Sudden Unexpected Death
Bibliographic record
Abstract
In forensic pathology, genetic testing is a luxury that is not readily entertained. With molecular advances, genetic testing can provide critical information to conclude cause and manner of death in otherwise sudden, undetermined deaths. An autopsy was performed on a 30-year-old woman who died suddenly three months postpartum. The patient's history was unremarkable and included a negative electrocardiogram test prior to pregnancy. Toxicology results were negative and there were no gross or histologic findings on a complete postmortem examination including the conduction system. Prompted by personal interest and knowledge, the spouse, an electrophysiology fellow, requested genetic testing be performed and personally financed the tests. The results revealed two genetic mutations strongly associated with Type 1 Long QT Syndrome, expected to be familial. This provided a cause and manner of death and also instigated preventive measures for testing in the child. This case supports previous studies that acknowledge the need for molecular testing in the autopsy-negative, sudden unexpected death cases in young patients. Forensic pathologists have a duty to the public to serve as their physicians and this may prove to be a key area that can contribute to the preventative medicine effort and provide closure to families that may have loved ones left with an undetermined cause and manner of death. Although financially a problem, the future of pathology is undeniably heading in the direction of molecular testing and an attempt to adjust an internal budget in order to include expenses for these tests should be considered.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".