Stakeholder perspectives on education in aortic dissection
Bibliographic record
Abstract
The Aortic Dissection (AD) Collaborative was established to evaluate patient-centered research priorities in AD. Education was identified as a topic of interest by the stakeholders. The AD Collaborative Education Working Group evaluated existing educational resources and identified areas amenable to comparative effectiveness research. The most important positive qualities of available AD education resources are ease of use, diversity of representation, accessibility, and organization. The most important negative qualities of these resources are non-patient-centered language, promotional themes, and those with limited applicability and accessibility. Through a series of focus groups, the Working Group identified target audiences for AD education and educational material content and critically assessed and prioritized barriers to effective AD education. Both the target audiences and the barriers include clinicians and patients themselves. The Working Group defined initiatives to overcome barriers, to include a comprehensive, universally agreed on AD resource that is updated in real time and making education accessible to all relevant target audiences. The Working Group then prioritized needs for comparative effectiveness research in AD education and determined that clinician education is the top priority for future efforts. The Working Group determined that assessment and evaluation of specific and appropriate screening strategies is the second most important priority. Finally, the Working Group identified patient education as the third most important priority, specifically determining how patients and their support groups learn best, the ideal strategies for information dissemination, and methods of assessing understanding and satisfaction with the education process.
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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 | 0.141 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".