A University Hospital Based Study on Thoracic Trauma: Life Threatening Event, Its Etiology, Presentation, and Management
Bibliographic record
Abstract
INTRODUCTION: Thoracic injury is becoming an important cause of mortality in Pakistan, especially in the younger subset of population. The initial management of these injuries is essential as these patients can develop multiple complications, ultimately leading to death of the patients. MATERIALS AND METHODS: This prospective observational study was carried out from January 2016 to December 2018 at the Department of Surgery, Holy Family Hospital, Rawalpindi Medical University, Pakistan. It included all the patients over 12 years of age who had thoracic trauma, who presented in the ED, and were admitted to the surgical ward and intensive care unit (ICU). Data were collected with the help of a pre-designed proforma. After relevant investigations and treatment, data were collected and analyzed through Statistical Package for Social Sciences (SPSS) for version 19. Nominal variables were reported as frequencies and percentages. RESULTS: Out of a total of 330 patients, 188 (56.9%) suffered from blunt injuries whereas 142 (43%) had penetrating injuries. The most common cause of these injuries was road traffic accidents -- 105 (32%) followed by falls -- 23 (76%). Most of the injuries encountered were isolated pneumothorax -- 74 (22.4%) followed by rib fractures with pneumothorax -- 71 (21.5%). Tube thoracostomy was done in 189 cases (57.3%) whereas 94 (28.5%) patients were managed conservatively. Complications were seen in 117 patients (35.4%). Out of these 117 cases, death was the major complication - 30 (25.6%) followed by bronchopleural fistula - 24 (20.5%) and empyema thoracis - 22 (18.8%). CONCLUSION: Road traffic accidents are a major cause of thoracic injuries in our setting. Tube thoracostomy is the most commonly used treatment modality. Mortality rate is high in the patients with thoracic injuries.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".