Incidence of Polytrauma in the Casualty Emergency Department of a Tertiary Care Hospital in Dhaka City
Bibliographic record
Abstract
Background: Polytrauma is a public health problem in every country. Objective: The purpose of the present study was to assess the incidence of Polytrauma in the Casualty Emergency Department of a tertiary care hospital in Dhaka city. Methodology: This cross-sectional study was carried out in the casualty of Emergency Department of Dhaka Medical College Hospital, Dhaka, Bangladesh. The patients who were admitted from January 2014 to December 2014 were included in this study. This study includes all the patients with polytrauma, most of whom underwent laparotomy or other types of surgery like suprapubic cystostomy, amputation, surgical toileting, chest drainage, but a small number was managed with non-operative approach. Immediately after admission, the patients were subjected to resuscitation, primary survey and history taking including pre-hospital retrieval. A tabulated sheet of questionnaire was given to all the patients and answer was taken by conversation and examination. Results: A total number of 100 patients were recruited for this study. The mean age with SD of the study population was 33.5±12.5 with the range of 3 to 67 years. The male female ratio is 7.33:1. The victims of polytrauma sustained their injury from different kinds of trauma. Out of hundred cases with polytrauma, road traffic accidents was the most common cause which included motor vehicle accident, pedestrian and motor cycle accident and resulted in a total of 63 casualty occupying 63% of the entire series. Polytrauma resulted fall from height occurred in 10 (10%) cases. Stab injury caused polytrauma in 5 (5%) cases. Conclusion: In conclusion majority of the study population is young adult with the predominance of male presented with road traffic accident. Journal of Current and Advance Medical Research, July 2023;10(2):60-64
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".