Stroke Prevention Strategies in the Developing World
Bibliographic record
Abstract
S troke is the second leading cause of death and disability in the world.1,2 During the past several decades, the burden of stroke in the world has shifted from developed to developing countries.3 Now, 75% of all stroke deaths and 81% of the total disability-adjusted life years lost because of stroke occur in developing countries.3 This shift in the burden from the developed to developing countries is thought to be driven by the aging of population, population growth, and changing patterns of diseases because of changes in risk factors and differences in socioeconomic status and health care.4,5 Stroke, therefore, has emerged as a major public health priority in developing countries. Challenges to Providing Healthcare for StrokeAlthough the burden of stroke has increased in developing countries, the health care services have not caught up.The challenges to provide health care services for stroke in developing countries include lack of awareness about stroke and its risk factors, lack of economic resources and publicly funded well functioning healthcare systems for primary and secondary prevention, lack of ambulance services and facilities for acute stroke management, unaffordable cost of tPA (tissuetype plasminogen activator), lack of rehabilitation facilities, preference for alternative and complementary medicines over modern medicines, and poor secondary prevention.6 These challenges often lead to worse outcomes after stroke, and studies in some regions of Gambia and India have reported 30-day case fatality as high as 40%.7,8 Also, a large percentage of people in developing countries live in rural areas where health care is not accessible.Authors of several studies have shown high mortality and prevalence of stroke in rural regions of developing countries.[9][10][11] The poor population in developing countries are often affected by stroke, and stroke perpetuates poverty in these people.Attempts are being made in developing countries to improve stroke services, but these are in very early stages.12 Therefore, urgent attention is needed to reduce the burden of stroke in developing countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".