Bibliographic record
Abstract
Both genome sequencing (GS) and exome sequencing (ES) have proven to be revolutionary in the diagnosis of pediatric rare disease. The diagnostic potential and increasing affordability make GS and ES more accessible as a routine clinical test in some centers. Herein, I review aspects of rare disease in pediatrics associated with the use of genomic technologies with an emphasis on the benefits and limitations of both ES and GS, complexities of variant classification, and the importance of genetic counseling. Indications for testing, the role of genetic counselors in genomic test selection, and the diagnostic potential of ES and GS in various pediatric multisystem disorders are discussed. The neonatal population represents an important cohort in pediatric rare disease. Rapid ES and GS in critically ill neonates can have an immediate impact on medical management and present unique genetic counseling challenges. This work includes reviews of recommendations for genetic counseling for families considering genome-wide sequencing, and issues of access to genetic counseling that affect clinical use and will necessitate implementation of innovative methods such as online decision aids. Finally, this work will also review the challenges of having a child with a rare disease, the impact of results from ES and GS on these families, and the role of various support agencies.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".