Animal Models of Repaired Tetralogy of Fallot: Current Applications and Future Perspectives
Bibliographic record
Abstract
Tetralogy of Fallot is the most common cyanotic congenital heart disease. Despite ongoing improvements in the initial surgical repair, there are lingering concerns regarding the long-term outcomes that may be complicated by right ventricular dysfunction, right ventricular dyssynchrony, and sudden cardiac death. The mechanisms leading to these late complications remain incompletely understood. Experimental animal models have been developed as preclinical steps to gain better insight into the pathophysiology of diseases and to develop new therapeutic strategies. This article summarizes the various types of experimental animal models of repaired tetralogy of Fallot published to date in the literature, with the aim of achieving a greater understanding of the deleterious mechanisms that may lead to these known late and sometimes lethal complications. In addition to analysing the type of animals that can be used according to a given study's objectives, needs, and constraints, the present review also evaluates the type of dysfunction that can be reproduced in our model according to the research objectives, as well as the different types of studies in which these models can be used. In view of all that, we propose a decision algorithm to create an animal model of repaired tetralogy of Fallot. This synthesis should furthermore help in the development of future studies and in the design of new experimental models, thus allowing greater insight into this disease, while not forgetting the ultimate goal of broadening future therapeutic measures to reduce the morbidity and mortality of this prevalent congenital heart disease.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".