National and sub-national trend and burden of injuries in Iran, 1990 - 2013: a study protocol.
Bibliographic record
Abstract
BACKGROUND: Worldwide, injuries are a major public health concern and make a considerable contribution to the disease burden. The present study is a component of the National and Subnational Burden of Diseases, Injuries, and Risk Factors from 1990 to 2013 (NASBOD) study in Iran, which was designed to investigate the burden of most important injuries (road traffic injuries, falls, burns, poisonings and drownings) at the national and sub-national levels in Iran. In this paper we explain definitions, organization, injuries selection process, data sources, data gathering methods, and data analyses of the national and sub-national burden of injuries study in Iran. METHODS: The burden of most important injuries in current metric of DALYs at the national and sub-national levels in Iran over 1990-2013 will be estimated through comprehensive reviews of either published or national data sources. Statistical modeling will be used to impute the missing data on the burden of selected important injuries for each district-year. CONCLUSION: The results of present study can help health policy makers to plan more comprehensive and cost-effective strategies at national and sub-national level for prevention and control of burden caused by 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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".