Safe management of bodies of deceased persons with suspected or confirmed COVID-19: a rapid systematic review
Bibliographic record
Abstract
INTRODUCTION: Proper strategies to minimise the risk of infection in individuals handling the bodies of deceased persons infected with 2019 novel coronavirus (2019-nCoV) are urgently needed. The objective of this study was to systematically review the literature to scope and assess the effects of specific strategies for the management of the bodies. METHODS: We searched five general, three Chinese and four coronavirus disease (COVID-19)-specific electronic databases. We searched registries of clinical trials, websites of governmental and other relevant organisations, reference lists of the included papers and relevant systematic reviews, and Epistemonikos for relevant systematic reviews. We included guidance documents providing practical advice on the handling of bodies of deceased persons with suspected or confirmed COVID-19. Then, we sought primary evidence of any study design reporting on the efficacy and safety of the identified strategies in coronaviruses. We included evidence relevant to contextual factors (ie, acceptability). A single reviewer extracted data using a pilot-tested form and graded the certainty of the evidence using the GRADE approach. A second reviewer verified the data and assessments. RESULTS: We identified one study proposing an uncommon strategy for autopsies for patients with severe acute respiratory syndrome. The study provided very low-certainty evidence that it reduced the risk of transmission. We identified 23 guidance documents providing practical advice on the steps of handling the bodies: preparation, packing, and others and advice related to both the handling of the dead bodies and the use of personal protective equipment by individuals handling them. We did not identify COVID-19 evidence relevant to any of these steps. CONCLUSION: While a substantive number of guidance documents propose specific strategies, we identified no study providing direct evidence for the effects of any of those strategies. While this review highlights major research gaps, it allows interested entities to build their own guidance.
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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 | 0.036 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".