Evaluation of a Mobile Application Tool to Assist Health Care Providers in Cardiovascular Risk Assessment and Management
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) is the leading cause of death worldwide.1 Unfortunately, CVD risk assessment and management (RAM) services face many challenges and barriers in the community. Mobile technology offers the opportunity to empower patients and improve access to health prevention strategies to overcome these barriers. 2 The purpose of this study was to pilot test the Arabic and English versions of the EPIRxISK™ CVD risk calculator in the public sector. Methods: Pilot testing of an Arabic and English version of the online application EPIRxISK™ for CVDRAM (Figure 1) was done by potential users from a sample consisting of the general population and pharmacists attending community pharmacies. Participants’ feedback was gathered in a qualitative interview which was recorded and transcribed for quality assurance and review by the research team. Responses from all interviews were analyzed and recommendations were made to finalize the application before phase II of the study. In phase II, quantitative and qualitative methods will be utilized to assess the feasibility of implementing a community pharmacy-based CVD risk assessment program using the English and Arabic versions of the EPIRxISK™ online application. Results: In phase I, a total of 9 pharmacists from community pharmacies and 5 general participants from the general population were interviewed. As shown in Table 1, the analysis of the interviews resulted in themes related to five frameworks: engagement, functionality, aesthetics, information, and subjective quality. Overall, the themes demonstrated acceptance and satisfaction with the features of the application. Phase II is currently in progress. Conclusion: The overall results of this study are indicative that the use of the EPIRxISK™ application for CVDRAM may be of benefit in Qatar, considering it is the first available in the Arabic language. The tool is likely well equipped to support continuous and standardized CVDRAM in Qatar's primary care sector. 3
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".