Introductory evidence on data management and practice systems of forensic autopsies in sudden and unnatural deaths: a scoping review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».