Introductory evidence on data management and practice systems of forensic autopsies in sudden and unnatural deaths: a scoping review
Bibliographic record
Abstract
Abstract Background The investigation into sudden unexpected and unnatural deaths supports criminal justice, aids in litigation, and provides important information for public health including surveillance, epidemiology, and prevention programs. The use of mortality data to convey trends can inform policy development and resource allocations. Hence, data practices/management systems in Forensic Medicine are critical. This study scoped literature and described the body of knowledge on data management and practice systems in Forensic Medicine. Methods Five steps of the methodological framework of Arksey and O’Malley guided this scoping review. A combination of keywords, Boolean terms, and Medical Subject Headings was used to search PubMed, EBSCOhost (CINAHL with full text and Health Sources), Cochrane Library, Scopus, Web of Science, Science Direct, WorldCat, and Google Scholar from the 18th to 24th of June 2020 and updated in November 2021 for peer review papers. This study included articles involving unnatural deaths, focused on data practice or data management systems, relating to Forensic Medicine, all study designs, and published in English. Screening and selection and data extraction were conducted by two reviews. Thematic analysis was conducted, and the results were reported using both quantitatively and qualitatively. Results Of the 23,059 articles, 16 met this study’s inclusion criteria. The included articles were published between 2008 and 2019. Eight of the 16 articles were published between 2017 and 2019. Most of the included studies were conducted in the USA (5) and Australia/New Zealand (4). Only two publications were from lower- and middle-income countries (Nigeria, Mexico), and the remaining 14 were from high-income countries (Italy, Denmark, USA, Australia, and New Zealand, Japan, Switzerland, Canada). The data management systems found in this study were as follows: Virtopsy, Canadian Coroner and Medical Examiner Database, Infant Injury Database, Fatal injury surveillance system, Medical Examiners and Coroners Alert System, National Violent Deaths Reporting System, AM/PM Database, Tokyo CDISC/ODM, and National Coronial Information System. Conclusions This study’s results revealed limited articles relating to data management and practice systems in Forensic Medicine, particularly in LMICs through literature indicating there is a prevalence of unnatural deaths in LMICs. This study, therefore, recommends research on data management and practice systems relating to forensic medicine in LMICs to inform policy decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".