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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".