Reaching Consensus for Unified Medical Language in Fontan Care
Bibliographic record
Abstract
AIMS: The Fontan operation has resulted in improved survival in patients with single-ventricle congenital heart disease. As a result, there is a growing population of teenagers and adults with a Fontan circulation. Many co-morbidities have been increasingly recognized in this population due to the unique features of the Fontan circulation. Standardization of how Fontan co-morbid conditions are defined will help facilitate understanding, consistency and interpretability of research and clinical experience. Unifying common language usage in Fontan is a critical precursor step for data comparison of research findings and clinical outcomes and ultimately accelerating improvements in management for this growing group of patients. This manuscript aimed to create unified definitions for morbidities seen after the Fontan palliation. METHODS: In association of many congenital heart disease organizations, this work used Delphi methodology to reach a broad consensus among recognized experts regarding commonly used terms in Fontan care and research. Each definition underwent at least three rounds of revisions to reach a final definition through surveys sent to experts in the field of single-ventricle care. RESULTS: The process of reaching a consensus on multiple morbidities associated with the Fontan procedure is summarized in this manuscript. The different versions that preceded reaching the consensus are also presented in the Supporting Information. Table 1 represents the final definitions according to the consensus. CONCLUSIONS: We propose the use of these definitions for clinical care, future research studies, registry development and clinical trials.
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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 | 0.436 | 0.461 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.007 | 0.034 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".