Comparative Study of Minimum Data Sets of Health Information Management of Organ Transplantation in Selected Countries and Presenting Appropriate Solution for Iran
Bibliographic record
Abstract
Introduction: Designing and performing of minimum data sets (MDS) in hospitals and health centers can be considered as the beginning steps of any disease information management which result in improvement of the quality of care and disease control. Transplantation MDS with a set of definitions considered as one the most important management decision making facilities which ensure accessibility to accurate health data. The main purpose of this work was to study the minimum data sets of health information management of organs transplantation in Canada, Malaysia and Australia, and present solutions for Iran. Methods: This research was an applied comparative study in 2009 which was conducted through three stages. At the first step, transplantation MDS in selected pioneer countries was studied via e-text and resources and compare with Iran situation systematically studied. At data analysis stage, the transplantation MDS at selected countries were compared using comparative tables method and difference and similarities were determined. Finally with respect to accumulated data, new organ transplantation registry forms were established for Iran. Results: A transplantation MDS was proposed as 23 forms in three categories (transplantation recipient registration forms, transplant candidate registration forms, transplant recipient follow-up forms, living donors and deceased donor registration forms) and approved by experts. Conclusion: In order to register all the possible data in future, it is necessary to establish different registry forms to prepare the most accurate set of data for organ procurement network. The prepared transplantation MDS as a set of forms will bring about costs controlling and promoting of organ transplantation management in Iran .Also assessment of the rate of organ donations survival establishment of appropriate polices will be possible through exploitation of extracted data from the prepared forms. Keywords: Data; Information Management; Transplants; Comparative Study.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".