Training in congenital interventional cardiology: interviews with experts from around the globe – part one
Bibliographic record
Abstract
The foundation of wisdom is rooted in experience, and thus we reflexively call upon our senior leaders, mentors, coaches, and family members for guidance in our personal and professional lives. Witnessing the weathered perspectives of others allows for an internal audit of one's own strengths and deficiencies, which ultimately inspires personal growth. This experience is heightened when both the mentor and the mentee, for example, share a common goal. The field of congenital interventional cardiology, with its constant evolution and diverse technical approaches, requires a lifetime of learning, as well as safe passage of knowledge to the next generation. While there are published recommendations for what to consider when completing this task, hearing the sentiments of those with experience may be more profitable for future fellows and current interventionalists. In part one of a series, we hope to accomplish this goal by presenting an opportunity to learn from our experienced colleagues on the topic of congenital interventional cardiology training. Specifically, we aim to share expert opinions on how to succeed as a congenital interventional fellow, illustrate the diversity of teaching styles and expectations in various healthcare systems, and for the mid-career interventionalists, provide insight into the character traits of a successful mentor of interventional fellows.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".