Next-generation sequencing for diagnosis of rare diseases in the neonatal intensive care unit
Bibliographic record
Abstract
BACKGROUND: Rare diseases often present in the first days and weeks of life and may require complex management in the setting of a neonatal intensive care unit (NICU). Exhaustive consultations and traditional genetic or metabolic investigations are costly and often fail to arrive at a final diagnosis when no recognizable syndrome is suspected. For this pilot project, we assessed the feasibility of next-generation sequencing as a tool to improve the diagnosis of rare diseases in newborns in the NICU. METHODS: We retrospectively identified and prospectively recruited newborns and infants admitted to the NICU of the Children's Hospital of Eastern Ontario and the Ottawa Hospital, General Campus, who had been referred to the medical genetics or metabolics inpatient consult service and had features suggesting an underlying genetic or metabolic condition. DNA from the newborns and parents was enriched for a panel of clinically relevant genes and sequenced on a MiSeq sequencing platform (Illumina Inc.). The data were interpreted with a standard informatics pipeline and reported to care providers, who assessed the importance of genotype-phenotype correlations. RESULTS: Of 20 newborns studied, 8 received a diagnosis on the basis of next-generation sequencing (diagnostic rate 40%). The diagnoses were renal tubular dysgenesis, SCN1A-related encephalopathy syndrome, myotubular myopathy, FTO deficiency syndrome, cranioectodermal dysplasia, congenital myasthenic syndrome, autosomal dominant intellectual disability syndrome type 7 and Denys-Drash syndrome. INTERPRETATION: This pilot study highlighted the potential of next-generation sequencing to deliver molecular diagnoses rapidly with a high success rate. With broader use, this approach has the potential to alter health care delivery in the NICU.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".