The comparative study of liver diseases surveillance system in selected countries
Bibliographic record
Abstract
Introduction: Today, liver disease is one of cause of morbidity, mortality and cost in word and global efforts to fight the liver diseases are undertaken. One of these attempts is to develop scientific methods and to initiate appropriate surveillance systems intended for the appreciation of the difficulties and the use of the most available data and models to present timely and practical information to make the appropriate decisions. This article has been performed with the aim of the comparative study of liver diseases surveillance system in selected countries. Method: In this descriptive study of applied kind, we initially investigated the existing Circumstances governing Iran's liver diseases information system referring to the "Disease Management Center" based in the Ministry of Health, Shiraz Namazi hospital, Tehran Imam Khomeini hospital and Esfahan Alzahra hospital, holding interviews, reviewing the associated forms, the existing documentation and checklist. Subsequently, the liver diseases surveillance systems of the selected countries are studied. Data analysis was conducted based on a comparison of the attributes of the liver diseases surveillance system in the countries under consideration using a descriptive and theorical analysis. Results: liver diseases surveillance system undertaken based study and experience existing in USA, Canada and Malaysia and country's requirement and incorporated along three axes: general specifications of the system, data source, the governing the collection and report of data and data processing. Conclusion: By considering to high prevalence of liver diseases in Iran and absence of comprehensive liver diseases surveillance systems, design and implementation of liver disease surveillance systems for disease management that quality control and cost control – as both high goal of health system the following is recommended
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".