P1-247 Cerebrovascular disease in 48 countries: secular trends in mortality 1950–2005
Bibliographic record
Abstract
Cerebrovascular disease (stroke) is the second cause of death and among the top five causes of morbidity in many developed and developing countries. The coincidence of trends of stroke and coronary heart disease mortalities is of question in different countries. This study aims to investigate patterns of increase and decrease of stroke mortality in 48 different countries. The mortality curves of stroke for 48 countries that had reliable data and met other selection criteria were examined using age-standardised death rates for 35–74 years from the WHO. Annual male mortality rates for individual countries from 1950 to 2005 were plotted and a table and graph were used to classify countries by magnitude, pattern and timing of stroke mortality. The natural history of stroke epidemics varies markedly among countries. Different stroke patterns are distinguishable; including “declining” (since the inception of data or 1950), “rise and fall”, “rising” (first part of epidemic), and “flat” (no epidemic yet). Further, epidemic peaks were higher in Asia, in particular Japan at 433/105, the former Soviet states at 388/105 and East Europe at 301/105 and lowest in Canada and Australia at 29/105. The different dates of mortality downturn could reflect the times when pharmaceutical treatment of hypertension started to be effective in sufficient numbers of the high risk population and/or there were significant changes in salt consumption. This could be translated to policy interventions for stroke control in countries with rising trend of the disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".