Necessity of National DRG System for the Cardiovascular Diseases in Iran
Bibliographic record
Abstract
ntroduction: It is necessary for every country considers establishing such a system considering to its goals, laws national, interests and standards. The main goal of this research was to compare the national cardiovascular disease DRG system in selected countries. This research is dealing with survey on system of Iran too. Methods: This descriptive-comparative study conducted in 2007-2008. The cardiovascular diseases of DRG systems in USA, Australia, England and Canada were reviewed. Data collected through literature review, Internet and e-mail, and presented in comparative tables. Results: Our findings showed that all of the selected countries have national cardiovascular diseases of the DRG systems. 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: Constituents of the model are: using a suitable committee for the cardiovascular DRG system in ministry of health and medical education, development coding standards, relationship between cardiologists and medical coders, presenting educational information about the national cardiovascular of the DRG system, using experiences of the developed 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".