Validation and clinical performance of a combined nuclear‐mitochondrial next‐generation sequencing and copy number variant analysis panel in a <scp>Canadian</scp> population
Bibliographic record
Abstract
Diagnosing mitochondrial disorders is a challenge due to the heterogeneous clinical presentation and large number of associated genes. A custom next generation sequencing (NGS) panel was developed incorporating the full mitochondrial genome (mtDNA) plus 19 nuclear genes involved in structural mitochondrial defects and mtDNA maintenance. This assay is capable of simultaneously detecting small gene sequence variations and larger copy number variants (CNVs) in both the nuclear and mitochondrial components along with heteroplasmy detection down to 5%. We describe technical validations of this panel and its implementation for clinical testing in a Canadian reference laboratory, and report its clinical performance in the initial 950 patients tested. Using this assay, we demonstrate a diagnostic yield of 18.1% of patients with known pathogenic variants. In addition to the common 5 kb mtDNA deletion, we describe significant contribution of pathogenic CNVs in both the mitochondrial genome and nuclear genes in this patient population.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".