Determination of the Incidence of Medicolegal Death in a Tertiary Health Institution in Abakaliki, Ebonyi State, South-East, Nigeria
Bibliographic record
Abstract
BACKGROUND: Death is an inevitable end that comes when not expected. However, when death occurs as a result of violence or unclear and suspicious manner, a coroner inquest is instituted to determine the cause, manner and the mechanism of death. AIM: To determine the incidence and causes of medicolegal death in Ebonyi State. MATERIALS & METHOD: This is a 5-year retrospective study of medicolegal autopsies reports of subjects whose cause of death were subject of litigation. The study analysed data between January 1, 2013, and December 31, 2017, at Alex Ekwueme Federal University Teaching Hospital, Ebonyi, Southeast, Nigeria. Data analysis was with the SPSS version 20. RESULTS: During the study period. A total number of 202 autopsies were performed. The age range of the deceased was from 2 years to 90 years, with a mean age of 35.2 ± 16.1. The predominant age group was 30 - 39 years (30.2%) while the least (0.5%) were between the age of 90 and 99 years. Males accounted for 158 (78.2%), and females were 44 (21.8%). Farmers (31.2%) and students (15.3%) were mostly affected by unnatural death in this study. Accidental deaths constituted 54.5% of cases, followed by homicidal death (36.6%). Impalement by sharp objects (41.9%) was a significant cause of death due to homicide in this study. In contrast, accidental deaths were mainly as a result of a road traffic accident (95.5%). The majority (60.4%) of those who died as a result of an accident sustained an injury at multiple body sites. CONCLUSION: Road traffic accident and homicide were responsible for the majority of cause of death found in medicolegal autopsies in Ebonyi State. Proper road maintenance, safe driving culture, and making people adhere strictly to the rule of law are necessary to reduce the incidence of avoidable deaths in our environment.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".