Epidemiology of the Upper Extremity Trauma in a Traumatic Center in Iran
Bibliographic record
Abstract
<p><strong>INTRODUCTION: </strong>Orthopedic injuries are the most common types of traumatic injuries and present<strong> </strong>as fractures of the limbs, pelvis, and vertebrae or lesions in soft tissues, muscles, ligaments, and tendons. The upper limb fractures occur in distal radius and ulna, metacarpals, fingers, scapula, and carpal bones, Proximal, diaphysis, distal humerus, clavicle, proximal ulna and radio and distal humero and humero, radio, ulna, and metacarpo. The objective of this project was to accurately describe the occurrence of injuries of the upper extremity and the mechanisms of such injuries in a representative sample of Iranian population.</p><p><strong>METHOD: </strong>This prospective case series was performed on the patients admitted to Shafa Yahyaian Hospital through the emergency ward within 6 months. Patients’ demographic features, the information about the mechanism of injury in soft tissues, bones and joints which obtained using clinical examination and imaging techniques, also the findings during the surgery were recorded in the information form. All analyses were performed using SPSS software, version 21. The independent t test or Mann-Whitney test and the Chi-square test or Fisher’s exact test was used to compare the data. The results were significant at P&lt;0.05.</p><p><strong>RESULTS: </strong>This study was performed on 1287 patients with upper limb fracture. The male and<strong> </strong>female patients respectively comprised of 998 (77.5%) and 289 (22.5%) subjects. About 113 patients suffered injuries at shoulder joint or its surrounding bones. The most common traumatic mechanisms in this group included falls from the standing position (49.2%), direct hit (19.5%), and then falling down (12.58%). Humerus fractures were observed in 68 patients. There was a significant correlation between humerus fractures and the mechanisms (P=0.000). The patients with traumas around the elbow comprised of 182 individuals. Sex distribution of fractures around the elbow shows a significant correlation between sex and elbow fractures. Forearm fractures were observed in 233 patients, and there was a significant correlation between age groups and forearm fractures. Fractures around the wrist were observed in 333 patients<strong>.</strong> There was a significant correlation between patients with fractures around the wrist (36.88±23.81 years) and patients without fractures (30.84±18.99 years) around the wrist in terms of the mean age. Hand fractures were observed in 358 patients<strong>.</strong> There was a significant correlation between hand fractures and sex<strong>.</strong></p><p><strong>CONCLUSION: </strong>The result of the current study which shows the epidemiology of these injuries and how such injuries occur in this area can well help the healthcare planners to design preventive and therapeutic measures.</p>
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".