Designing the Coronary Artery Disease Registry with Data Management Processes Approach: A Comparative Systematic Review in Selected Registries
Bibliographic record
Abstract
: Context The use of registries to Coronary Artery Disease (CAD) data management plays an important role in the improvement of healthcare processes and reduction of outcomes for patients and healthcare providers. The present study aimed to compare the data management processes of CAD registries in the selected countries. Evidence Acquisition This review study was conducted comparatively in 2019. After selecting countries based on some criteria, the required data were collected by searching valid databases, more useful search engines, and related websites to CAD registries for the selected countries as well as by sending E-mails containing a data extraction form to the related organizations. Results Totally, five registries were chosen in the selected countries as follows: CADOSA (Australia), APPROACH (Canada), START (Italy), CLARIFY (Spain), and GWTG-CAD (US). The results showed that 60% of the selected registries made use of the electronic case report form for data gathering. The main data elements included demographic and general information, risk factors, vital sings, medication, laboratory tests results, examination results, ECG results, invasive measures and interventions, patient’s status on discharge, results of follow-ups, and post-discharge outcomes. Conclusion Developing CAD registries based on the data management principles provides the context to conduct cohort studies with very low costs. With regard to the study results, attention should be paid to data management processes, include data gathering, data processing, and information distribution, in development of CAD registries.
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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 | 0.057 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".