Autopsy findings of firearm fatalities at District Head Quarter hospital, Lakki Marwat, KP
Bibliographic record
Abstract
Introduction: Firearm injuries are considered one of the leading causes for both homicidal and suicidal deaths worldwide. Among different firearms, handguns are the most preferred weapon used especially for suicide. Frequency of firearm fatalities vary from place to place depending upon multiple factors like culture, literacy rate, strict execution of laws.Material & Methods: This was a descriptive cross-sectional study based on secondary data of medico-legal autopsy reports of years 2013 and 2014 at the District Head Quarter (DHQ) Hospital Lakki Marwat, Khyber Pakhtunkhwa (KP), Pakistan. Informed consents were collected from respective authorities. All reports were analyzed in SPSS version 20 for descriptive statistics.Results: A total of 93 autopsies of firearm fatalities including 49(52.7%) cases in 2013 and 44(47.3%) cases in 2014 were analyzed, which included 85(91.4%) males. The mean age of the deceased was 31.5±13.3 years. Majority of cases were brought from Lakki Marwat, followed by Ghazni Khel region. Multiple gunshot wounds were found in 53(57%) cases. Chest was the most affected body area involved, followed by head. Homicide accounted for 90(96.8%) and suicide for 03(3.2%) cases.Conclusion: Young males were the preferred victims of fatal gunshot wounds, with majority being cases of homicide sustaining multiple wounds on upper parts of the body.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".