The coronary heart team
Bibliographic record
Abstract
PURPOSE OF REVIEW: The concept of a Coronary Heart Team has generated increased interest, including support from major practice guidelines. Here, we review the rationale and the published experience of Coronary Heart Teams. RECENT FINDINGS: A Coronary Heart Team should be led by both cardiology and cardiac surgery with a shared decision-making approach. The team should incorporate data from anatomic and clinical risk prediction models to offer individualized care. Most teams focus on management of complex patients and those with indications for both coronary artery bypass graft and percutaneous coronary intervention. The potential benefits of a Coronary Heart Team include balanced decision-making, greater adherence to evidence-based practice guidelines, as well as promoting greater collegiality and exchange of knowledge between specialties. Single-center series have demonstrated consistency in decision-making by Coronary Heart Teams but prospective data demonstrating improved patient outcomes and/or cost effectiveness are necessary. SUMMARY: The concept of a Coronary Heart Team is gaining traction for patients with complex coronary artery disease. There is a growing literature in support of Coronary Heart Teams but comparative and prospective data demonstrating improved patient outcomes are needed.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".