Bibliographic record
Abstract
PURPOSE OF REVIEW: Advances in genomics have paved the way for personalized medicine applications. This review will discuss new discoveries in genomics and pharmacogenomics in children with congenital heart disease (CHD) and the application towards the development of new diagnostics, disease risk predictions, and optimizing response to drugs and surgery. RECENT FINDINGS: Recent advances have identified common and rare variants associated with complex CHD using next-generation sequencing and genotyping technology. Next-generation sequencing is now being used not only for clinical genetic testing but also for noninvasive prenatal testing of fetal DNA in maternal serum to diagnose genetic conditions like fetal aneuploidies as early as the first trimester. This approach is not only more accurate but also safer than invasive maternal screening tests. This technology may also help in noninvasive diagnosis of transplant rejection. As genetic variations that influence the response to surgery in CHD are identified, this can guide decision-making surrounding optimum type and timing of surgery. Drug choice and dosing are being increasingly influenced by knowledge of pharmacogenetic and pharmacodynamic variations. Age-related and maturation-related changes in drug pharmacokinetics make it crucial to perform pediatric-targeted pharmacogenetic studies to enable the incorporation of age into genotype-guided drug dosing algorithms. SUMMARY: Rapid genomic and pharmacogenomic discovery are guiding the development of more sensitive screening and diagnostic tests for CHD as well as development of safer and more effective drugs. This needs to be paralleled by the development of strategies to support rapid translation of emerging genomic knowledge to patient care.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 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".