The state of transcatheter aortic valve implantation training in Canadian cardiac surgery residency programs
Bibliographic record
Abstract
BACKGROUND: The current state of transcatheter aortic valve implantation (TAVI) training for Canadian cardiac surgical residents is unknown. Our goals were to establish a national inventory of TAVI educational resources, elucidate the role of residents in TAVI programs, and determine the attitudes and perspectives of residents and program directors regarding the importance of TAVI technology and training. METHODS: We sent Web-based surveys and reminders to all Canadian cardiac surgical residents and program directors between February and July 2017. We used descriptive analyses to summarize data in an aggregate and anonymous manner. We analyzed patterned responses to open-ended survey questions using thematic analysis. RESULTS: Seventy-eight of 92 residents (85%) and 11 of 12 program directors (92%) completed the survey, with broad representation from across Canada. A minority of residents (14 [18%]) and program directors (4 [36%]) reported that TAVI training in their program was adequate. Only 3 program directors (27%) reported that their residents had access to TAVI simulation training. Although most residents (76 [97%]) and program directors (10 [91%]) agreed that TAVI was important to the trainee's future practice, about two-thirds (54 [69%] and 7 [64%], respectively) agreed that TAVI should be a focus of fellowship training. A perceived lack of interest from interventional cardiologists to teach surgical residents, competition from TAVI fellows and lack of formalized time during residency were identified as perceived barriers to TAVI training. CONCLUSION: As Canadian surgical residency training moves toward a Competence by Design curriculum, there remains a pressing need to create uniform learning objectives and expectations in the TAVI curriculum.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".