Bibliographic record
Abstract
Due to improved survival and clinical outcomes, congenital heart disease (CHD) is an area of growing importance within the medical community. As these patients reach adulthood and have children, there has been a growing appreciation for the increased risk of CHD among their offspring, strongly implying a genetic element. Given the growing wealth of genetic data available and these clinical implications, this review serves to reexamine the role of genetics within CHD, using Tetralogy of Fallot as a model pathology. Tetralogy of Fallot (TOF) is one of the oldest documented CHDs, with a growing prevalence of adult patients, and thus serves as an excellent model for this review. Given the complex nature of cardiac development, it is not surprising that multiple transcription factors and signaling molecules responsible for cardiogenesis have been implicated in TOF, with additional, previously nonimplicated genes being routinely reported within the literature. This review focuses on the well-characterized genes gata4, nkx2.5, jag1, foxc2, tbx5, and tbx1, which have been previously implicated in TOF. Furthermore, this article will attempt to summarize the specific clinical implications associated with the affected genes, such as right-sided aortic arches, associated syndromic presentations, and parental carrier states.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".