Comparative study on National Burn Registry in America, England, Australia and Iran
Bibliographic record
Abstract
CONTEXT: Iran experiences a high rate of burns accompanied by painful consequences, death and a lot of disabilities. In order to reduce the burden of this injury, some strategies such as designing and implementation of registration systems are essential. AIMS: The aim of this study was to compare National Burn Registry in America, England, Australia and Iran. MATERIALS AND METHODS: This study was comparative-descriptive in which data collected from the National Burn Registry of America, England, Australia and Iran studied in 2013. The study population included National Burn Registry of these countries and data was collected using raw data forms. STATISTICAL ANALYSIS USED: Data on each country was categorized according to objectives and comparisons took place using comparative tables. Finally, descriptive-theoretical analysis of the findings was performed. RESULTS: National Security Agency and National Burn Repository in America, National Institute of Health and the Ministry of Health in England and the Department of Health and Senior in Australia are responsible for national burning registry. A seven-axial model was proposed for Iran's National Registry. America's registry system is broader than other countries due to its cooperation with Canada, Sweden and Asia. CONCLUSION: The aim of the Burn Registry System is to gather, store, edit, categorize, analyze and distribute all burns, injured data from all health care centers in a specific population and provide valuable information about the occurrence, time and regional distribution of burn injury.
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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 | 0.001 | 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".