ANALYSIS OF PATTERN AND SEVERITY OF INJURIES IN MEDICO LEGAL CASES PRESENTING TO EMERGENCY DEPARTMENT OF A TERTIARY CARE HOSPITAL: A RETROSPECTIVE STUDY
Bibliographic record
Abstract
Medico-legal cases presented to the Emergency Department often involve a diverse range of injuries and understanding the patterns and severity of these injuries is crucial for medical practitioners and the legal authorities. This retrospective study aims to analyse and interpret the nature, distribution, and severity of injuries sustained by individuals in medico-legal cases presenting to the emergency department. A total of 692 medico-legal cases recorded in the medico-legal register of our hospital were included in this study during study period. Data related to patient demographics, injury characteristics, and clinical outcomes were collected and analysed; observed, discussed and compared with other studies. The demographic analysis reveals a predominant occurrence of cases in the age group of 20-40 years (68.49%), with a notable male predominance (79.77%). Seasonal variations indicate a peak in cases during October (16.47%) and reduced incidences during April (1.01%). Road traffic accidents (31.21%) and physical assault (24.85%) emerged as the leading causes, while sexual assault cases were notably absent. Abrasions (43.54%) constitute the most common mechanical injury,
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".