Remote certification of out-of-hospital deaths in a Canadian Province: An 8-year experience of a novel practice
Bibliographic record
Abstract
Introduction: Certification of out-of-hospital deaths is challenging as physicians are often unavailable at the scene. In these situations, emergency medical services will generally transport the decedent to the nearest hospital. In 2011, a remote death certification program was implemented in the province of Québec, Canada. The program was managed through an online medical control center and enabled death certification by a remote physician. We sought to evaluate the implementation and feasibility of the remote death certification program and to describe the challenges we experienced. Methods: We retrospectively reviewed all remote death certification requests received at the online medical control center between 2011 and 2019. Data were collected from the online medical control center database and records. Feasibility was determined by evaluating the remote death certification rate. Results: Overall, 84.1% of remote death certification requests were realized, producing a total of 9776 death certificates. Male decedents accounted for 61.5% of remote death certification requests and were more likely than females to undergo a coroner’s investigation for cause of death (36.3% vs 20.8%, p = 0.017). Urban/mixed regions had higher rates of achieved remote death certifications (mean 87.3% vs 76.9%, p = 0.033) and putrefied bodies (mean 3.8% vs 2.2%, p = 0.137) compared to rural regions. Among unrealized remote death certification requests, the most common reason was failure of relatives to designate a funeral home (36.8%). Conclusion: Our 8-year experience with the remote death certification program demonstrates that despite facing numerous challenges, this process is feasible and offers a valuable option to manage out-of-hospital deaths. The remote death certification program is spreading in the remaining regions of Québec. Future studies will aim to quantify how much time this process saves for emergency medical services in each region of the province.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".