Comparison of cardiovascular diseases classification system in selected countries with Iran
Bibliographic record
Abstract
**** iAbstract Background: Availability of an accurate information system in recording and analysis of medical findings obtained from patients with cardiovascular diseases plays a crucial role in fighting cardiovascular diseases. One of the most important approaches in collecting such information is the classification system of cardiovascular diseases. Objective: The main goal of this research was to provide effective strategies for establishing a national classification system for cardiovascular diseases in Iran through a comparative study of such national systems currently available in USA, Australia, Canada, and England. Methods: This was a descriptive-comparative study conducted during 2007-2008. The classification systems of cardiovascular diseases in USA, Australia, England and Canada were reviewed to provide an appropriate model for Iran. Data collection was performed through literature review, Internet and e-mails. Findings: Our findings showed that all selected countries benefit a national classification system for cardiovascular diseases. At present, the systems named as ICD-10-CM in USA, ICD-10-AM in Australia, Rcc in England, ICD-10-CA in Canada, and ICD-10 in Iran are in use. This research attempted to discuss the advantages and the constraints of each system mentioned earlier. Conclusion: Considering our domestic needs, policies and health system priorities, establishing a national classification system for cardiovascular diseases seems to be necessary.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".