The Comparison of the Cardiovascular Diseases Procedures of the Classification Systems, in Selected Countries with Iran
Bibliographic record
Abstract
Introduction: Cardiovascular diseases are important factors at human death and factor destroys human resources. Health intervention is a service for developing of health and diagnoses or modifies of process diseases. It is necessary that every country regarding to its goals, laws national, interests and standards, considers establishing such a system. A comparison of cardiovascular disease classification systems in selected countries with that in Iran could assist health care policy makers in their efforts to battle cardiovascular diseases. Methods: This descriptive-comparative study conducted 2007-2008. The cardiovascular diseases of the classification systems in USA, Australia, England and Canada were reviewed in order to prepare an appropriate model for Iran. Data collecting was done through literature review, Internet and email. Results: The cardiovascular disease classification systems of all the selected countries are national. The selected countries, with the exception of England, utilize a multiaxial 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. Conclusion: It seems essential that the cardiovascular disease classification system in health interventions in Iran be national if improvements are to be made in the fields of prevention and management of these diseases. Keywords: Cardiovascular Diseases; Classification; Health.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".