ANALYSIS OF PATTERN AND SEVERITY OF INJURIES IN MEDICO LEGAL CASES PRESENTING TO EMERGENCY DEPARTMENT OF A TERTIARY CARE HOSPITAL: A RETROSPECTIVE STUDY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it