Bibliographic record
Abstract
Congenital heart disease (CHD) is the most common type of birth defect. Traditionally, a polygenic model defined by the interaction of multiple genes and environmental factors was hypothesized to account for different forms of CHD. It is now understood that the contribution of genetics to CHD extends beyond a single unified paradigm. For example, monogenic models and chromosomal abnormalities have been associated with various syndromic and non-syndromic forms of CHD. In such instances, genetic investigation and testing may potentially play an important role in clinical care. A family tree with a detailed phenotypic description serves as the initial screening tool to identify potentially inherited defects and to guide further genetic investigation. The selection of a genetic test is contingent upon the particular diagnostic hypothesis generated by clinical examination. Genetic investigation in CHD may carry the potential to improve prognosis by yielding valuable information with regards to personalized medical care, confidence in the clinical diagnosis, and/or targeted patient follow-up. Moreover, genetic assessment may serve as a tool to predict recurrence risk, define the pattern of inheritance within a family, and evaluate the need for further family screening. In some circumstances, prenatal or preimplantation genetic screening could identify fetuses or embryos at high risk for CHD. Although genetics may appear to constitute a highly specialized sector of cardiology, basic knowledge regarding inheritance patterns, recurrence risks, and available screening and diagnostic tools, including their strengths and limitations, could assist the treating physician in providing sound counsel.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".