Comparison of Cardiovascular Diseases Classification in some Developed Countries with Iran
Bibliographic record
Abstract
Background and Aim: Today, cardiovascular diseases are the most important public health challenges in the world. Prevention and management of cardiovascular diseases necessitate the existence of a comprehensive system for recording data. Patient medical document is one of the most important data which should be classified so to facilitate and expedite the treatment process. A comparison of cardiovascular disease classification systems could assist health care policy makers to battle cardiovascular diseases.Materials and Methods : This descriptive-comparative study conducted during years 2007-2008. The cardiovascular diseases classification systems in USA, Australia, England and Canada were reviewed. Data collecting was done through literature review, Internet and e-mail.Results : The cardiovascular disease classification systems of all the developed countries are national. The developed countries, with the exception of England, utilize a multiracial classification model, especially designed to reflect the individual requirements of every single one of them. This model employs health care standards, e-learning, annual educational programs, and consultation with experts. Iran lacks such a national classification system for cardiovascular diseases.Discussion and Conclusion : In order to improve the management and prevention of cardiovascular diseases in Iran, it is essential that the cardiovascular disease classification system in the country be national.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".