Nanopore long-read sequencing for the critically ill facilitates ultrarapid diagnostics and urgent clinical decision making
Bibliographic record
Abstract
Critically ill pediatric patients often have genetic disorders requiring a rapid diagnosis to guide urgent care decisions. Standard genetic testing typically takes weeks and requires multiple tests. Nanopore long-read genome sequencing (LR-GS) delivers genome-wide results within days as a one-test-fits-all solution. As one of the first centers in Europe, we implement ultrarapid LR-GS for critically ill patients. We enrolled 26 critically ill patients (median age 2 months) suspected of having a genetic disorder at the intensive care unit to perform (ultra)rapid nanopore LR-GS alongside standard genomic care. We compared diagnostic yield, turnaround time (TAT), and evaluated the impact on clinical decision making. In 11/26 cases a genetic diagnosis was made with (ultra)rapid LR-GS. From sample receipt to result, the average TAT was 5.3 days (range 2.0-10.8) for LR-GS and 18.4 days (range 6.1-29.1) for standard genomic care. DNA methylation analysis from LR-GS expedited the diagnosis in 3/26 cases. In 7/11 solved cases ultrarapid LR-GS led to immediate adjustments in patient care, e.g., medication switch or termination of treatment. Our findings underscore the clinical impact of ultrarapid LR-GS, including added value of methylation analysis, for critically ill patients and highlight existing challenges, paving the way to ultrarapid LR-GS integration into standard diagnostics.
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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 | 0.001 | 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".