The Many Care Models to Treat Thoracic Aortic Disease in Canada: A Nationwide Survey of Cardiac Surgeons, Cardiologists, Interventional Radiologists, and Vascular Surgeons
Bibliographic record
Abstract
BACKGROUND: Several specialties treat thoracic aortic disease, resulting in multiple patient care pathways. This study aimed to characterize these varied care models to guide health policy. METHODS: A 57-question e-survey was sent to staff cardiac surgeons, cardiologists, interventional radiologists, and vascular surgeons at 7 Canadian medical societies. RESULTS: For 914 physicians, the response rate was 76% (86 of 113) for cardiac surgeons, 40% (58 of 146) for vascular surgeons, 24% (34 of 140) for radiologists, and 14% (70 of 515) for cardiologists. Several services admitted type B dissections (vascular 37%, cardiology 31%, cardiac 18%, other 7%), and care was heterogeneous. Ownership of disease management was overestimated relative to the perspective of the other specialties. Type A dissection admissions and treatment were more uniform, but emergent call coverage varied. A 24/7 aortic specialist on-call schedule was present only 4% of the time. "Aortic" case rounds promoted attendance by a broader aortic specialty contingency relative to rounds that were specialty specific. Although 89% of respondents felt an aortic team was best for patient care, only 54% worked at an institution with an aortic team present, and only 28% utilized an aortic clinic. Questions designed to define an aortic team derived 63 different combinations. CONCLUSIONS: Thoracic aortic disease follows a network of undefined and variable care pathways, despite its high-risk population in need of complex treatment considerations. Multidisciplinary aortic teams and clinics exist in low volume, and the "aortic team" remains an obscure construct. A multispecialty initiative to define the aortic team and outline standardized navigation pathways within the health systems hospitals is advocated.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".