A survey of cardiac surgeons to evaluate the use of sutureless aortic valve replacement in Canada
Bibliographic record
Abstract
BACKGROUND: Sutureless aortic valve replacement (SuAVR) is gaining popularity for the treatment of aortic stenosis. We aimed to describe Canadian cardiac surgeons' practice patterns and perceptions regarding SuAVR. METHODS: Content experts (clinicians and methodologists) developed the survey. Domains in the questionnaire include: respondent characteristics, factors influencing the decision to implant a SuAVR, barriers to SuAVR use, and interest in participating in a trial. RESULTS: A total of 66 cardiac surgeons (median duration of practice: 15 years; range 8-20 years) from 18 hospitals across Canada responded to the survey for a response rate of 84%. Surgeons reported that the following patient characteristics increased the likelihood they would choose SuAVR: hostile root (73%), small annular size (55%), high Society of Thoracic Surgery risk score (42%), older age (40%), to support minimally invasive surgery (25%) and redo-operation (23%). The following patient characteristics made surgeons less likely to pursue SuAVR: young age (73%), low STS score (40%), and large annular size (30%). Reported barriers to SuAVR use included: cost (33%), permanent pacemaker risk (27%) and uncertain durability (12%). Of respondents, 73% were interested in participating in a randomized controlled trial comparing SuAVR with transcatheter aortic valve replacement. CONCLUSIONS: The primary reasons for surgeons selecting SuAVR were high surgical risk and anatomical challenges. Cost is a primary factor limiting SuAVR use.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".