Personalized vascular healthcare: insights from a large international survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Fragmentation of healthcare systems through limited cross-speciality communication and intermittent, intervention-based care, without insight into follow-up and compliance, results in poor patient experiences and potentially contributes to suboptimal outcomes. Data-driven tools and novel technologies have the capability to address these shortcomings, but insights from all stakeholders in the care continuum remain lacking. A structured online questionnaire was given to respondents (n = 1432) in nine global geographies to investigate attitudes to the use of data and novel technologies in the management of vascular disease. Patients with coronary or peripheral artery disease (n = 961), physicians responsible for their care (n = 345), and administrators/healthcare leaders with responsibility for commissioning/procuring cardiovascular services (n = 126) were included. Narrative themes arising from the survey included patients’ desire for more personalized healthcare, shared decision-making, and improved communication. Patients, administrators, and physicians perceived and experienced deficiencies in continuity of care, and all acknowledged the potential for data-driven techniques and novel technologies to address some of these shortcomings. Further, physicians and administrators saw the ‘upstream’ segment of the care journey—before diagnosis, at point of diagnosis, and when determining treatment—as key to enabling tangible improvements in patient experience and outcomes. Finally, despite acceptance that data sharing is critical to the success of such interventions, there remains persistent issues related to trust and transparency. The current fragmented care continuum could be improved and streamlined through the adoption of advanced data analytics and novel technologies, including diagnostic and monitoring techniques. Such an approach could enable the refocusing of healthcare from intermittent contacts and intervention-only focus to a more holistic patient view.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 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 it