Tendency of unnatural deaths in Faisalabad city during 2023- a retrospective study.
Bibliographic record
Abstract
ABSTRACT: BACKGROUND & OBJECTIVE: Unnatural deaths reflect the social and mental setup of a society. The objective was to assess the trend of unnatural deaths in Faisalabad during the year 2023 and determine the gender and age group mostly affected and the means employed. METHODOLOGY: This cross-sectional descriptive study was carried out in the Department of Forensic Medicine and Toxicology, Punjab Medical College, Faisalabad, from the data of all victims of unnatural deaths from 1st January 2023 to 31st December 2023. Data was collected from respective official Police and post-mortem reports and recorded on pre-structured proformas and categorized based on the manner of death, type of weapon, age groups involved, and gender. RESULTS: Out of a total of 266 autopsies, 227 were males and 39 females (5.8:1). Majority of the victims belonged to the 30-39 -year age group i.e. 100 (37.6%) followed by the 20-29-year age group with 49 (18.4%) cases. The manner of death was homicidal in 198 (74.4%) cases followed by 46 (17.3%) accidental and 14 (5.3%) suicide while the manner of death remained undetermined in 5 (1.8%) cases. Firearms claimed 116 (43.6%) lives followed by poisoning (61,22.9%) cases. October experienced the maximum number of cases (30,11.3%). CONCLUSION: Males are the major victims of unnatural deaths especially individuals in the middle age group. Firearm weapons are the major weapon of assault due to their easy availability. There is a dire need for strict implementation of laws on buying and possession of firearms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".