Exome and Genome Sequencing to Diagnose the Genetic Basis of Neonatal Hypotonia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Hypotonia is a relatively common finding among infants in the neonatal intensive care unit (NICU). Consideration of genetic testing is recommended early in the care of infants with unexplained hypotonia. We aimed to assess the diagnostic yield and overall impact of exome and genome sequencing (ES and GS). METHODS: Consecutive infants with hypotonia were identified from research and clinical databases across 5 teaching hospitals in United States, Canada, United Kingdom, and Australia. Inclusion criteria included NICU admission and genetic evaluation. Infants with a known explanation for hypotonia were excluded. Data regarding infant characteristics, genetic testing, and diagnoses were collected. The primary outcome was identification of a molecular diagnosis. Impact on care was a secondary outcome. The Fisher exact and Wilcoxon rank-sum tests were used for statistical analysis. RESULTS: = 1.0E-05). DISCUSSION: Rapid ES and GS provided a molecular diagnosis for most of the infants with unexplained hypotonia who underwent testing. Further studies are needed to assess the generalizability of these findings as increased access to genetic testing becomes available. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that in unexplained neonatal hypotonia, rapid ES or GS adds diagnostic specificity.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".