Use of a Heart Team in Decision-Making for Patients with Complex Coronary Disease at Hospitals in Michigan Prior to Guideline Endorsement
Bibliographic record
Abstract
BACKGROUND: Revascularization decisions can profoundly impact patient survival, quality of life, and procedural risk. Although use of Heart Teams to make revascularization decisions is growing, data on their implementation in the real-world are limited. Our objective was to assess the prevalence of Heart Teams and their association with collaboration in routine practice. METHODS: A survey of cardiologists and cardiac surgeons at 31 hospitals in Michigan was performed in May, 2011--prior to the recommendation for using Heart Teams in national guidelines. This survey included all percutaneous coronary intervention-performing hospitals in Michigan participating in the Blue Cross/Blue Shield of Michigan Cardiovascular Consortium and Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative. It targeted both the use of Heart Teams and multidisciplinary Case Conferences. RESULTS: There were 53 physician survey respondents from 27 hospitals with 4 hospitals not responding. Among respondents, 11 (40.7%) hospitals reported no Heart Teams or Case Conferences while 7 (25.9%) hospitals reported either a Heart Team or Case Conference. However, there was disagreement about the presence of a Heart Team at seven hospitals, and about Case Conferences at nine hospitals. Hospitals with definite Heart Teams reported significantly greater levels of collaboration between cardiologists and cardiac surgeons. CONCLUSION: The overall presence of Heart Teams prior to their recommendation in national guidelines was limited. Even among hospitals with a potential Heart Team, there was substantial disagreement between respondents about their presence. Further refinement of the definition of a Heart Team and measures of successful implementation are needed.
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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 | 0.011 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".