Causes of Death Data in the Global Burden of Disease Estimates for Ischemic and Hemorrhagic Stroke
Bibliographic record
Abstract
BACKGROUND: Stroke mortality estimates in the Global Burden of Disease (GBD) study are based on routine mortality statistics and redistribution of ill-defined codes that cannot be a cause of death, the so-called 'garbage codes' (GCs). This study describes the contribution of these codes to stroke mortality estimates. METHODS: All available mortality data were compiled and non-specific cause codes were redistributed based on literature review and statistical methods. Ill-defined codes were redistributed to their specific cause of disease by age, sex, country and year. The reassignment was done based on the International Classification of Diseases and the pathology behind each code by checking multiple causes of death and literature review. RESULTS: Unspecified stroke and primary and secondary hypertension are leading contributing 'GCs' to stroke mortality estimates for hemorrhagic stroke (HS) and ischemic stroke (IS). There were marked differences in the fraction of death assigned to IS and HS for unspecified stroke and hypertension between GBD regions and between age groups. CONCLUSIONS: A large proportion of stroke fatalities are derived from the redistribution of 'unspecified stroke' and 'hypertension' with marked regional differences. Future advancements in stroke certification, data collections and statistical analyses may improve the estimation of the global stroke burden.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".