Exploring postmortem practices for cardiac device interrogation in the UK
Bibliographic record
Abstract
INTRODUCTION: Ascertaining the cause of sudden death is often difficult, even in patients with a history of heart disease. Device interrogation is routinely carried out to determine the nature of symptoms or aborted sudden death episodes in patients with cardiac implantable electronic devices (CIED), but it is not clear how often this is done postmortem, and if so, how this information is recorded and used by clinical teams and coroners. METHODS: We determined the proportion of deaths with a CIED in situ, the capacity for and frequency of postmortem device interrogation and when done, how the information was used over a 5-year period in the UK by surveying 173 National Health Service (NHS) trusts via a freedom of information request. RESULTS: A response was received from 83 (48%) NHS sites, 75 (90%) of which reported having both a mortuary and cardiac physiology department onsite. During the period 2019-2024, each mortuary handled 2400±1094 deaths per annum, of which an estimated 5±2% had a CIED in place. Of those with cardiac physiology on site, only 15 (20%) reported routine postmortem device checks were performed. If such a check was conducted, 3 out of 15 (20%) responded that findings were documented in the medical records and 2 out of 15 (13%) stated information was relayed to the medical team. CONCLUSION: Although 1 in 20 patients who present to NHS mortuaries have a CIED in situ, routine postmortem checks are performed rarely and inconsistently documented. Prospective studies are warranted to determine the feasibility and utility of standardised postmortem device interrogation.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".