Regionalization of congenital cardiac surgical care: what it will take
Bibliographic record
Abstract
PURPOSE OF REVIEW: Decentralized, inconsistent healthcare delivery results in variable outcomes and wastes nearly one trillion dollars annually in the United States (US). Congenital heart surgery (CHS) is not immune due to high, variable costs and inconsistent outcomes across hospitals. Many European countries and Canada have addressed these issues by regionalizing CHS. Centralizing resources lowers costs, reduces in-hospital mortality and improves long-term survival. Although the impact on travel distance for patients is limited, the effect on healthcare disparities requires study. This review summarizes current data and integrates these into paths to regionalization through health policy, research, and academic collaboration. RECENT FINDINGS: There are too many CHS programs in the US with unnecessarily high densities of centers in certain regions. This distribution lowers center and surgeon case volumes, creates redundancy, and increases variation in costs and outcomes. Simultaneously, adhering to suboptimal allocation impedes the understanding of optimal regionalization models to optimize congenital cardiac care delivery. SUMMARY: CHS regionalization models developed for the US increase surgeon and center volume, decrease healthcare spending, and improve patient outcomes without substantially increasing travel distance. Regionalization in countries with few or no existing CHS programs is yet to be explored, but may be associated with more efficient spending and procedural complexity expansion.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".