Improving diagnostic precision, care and syndrome definitions using comprehensive next-generation sequencing for the inherited bone marrow failure syndromes
Bibliographic record
Abstract
BACKGROUND: Phenotypic overlap among the inherited bone marrow failure syndromes (IBMFSs) frequently limits the ability to establish a diagnosis based solely on clinical features. >70 IBMFS genes have been identified, which often renders genetic testing prolonged and costly. Since correct diagnosis, treatment and cancer surveillance often depend on identifying the mutated gene, strategies that enable timely genotyping are essential. METHODS: To overcome these challenges, we developed a next-generation sequencing assay to analyse a panel of 72 known IBMFS genes. Cases fulfilling the clinical diagnostic criteria of an IBMFS but without identified causal genotypes were included. RESULTS: The assay was validated by detecting 52 variants previously found by Sanger sequencing. A total of 158 patients with unknown mutations were studied. Of 75 patients with known IBMFS categories (eg, Fanconi anaemia), 59% had causal mutations. Among 83 patients with unclassified IBMFSs, we found causal mutations and established the diagnosis in 18% of the patients. The assay detected mutant genes that had not previously been reported to be associated with the patient phenotypes. In other cases, the assay led to amendments of diagnoses. In 20% of genotype cases, the results indicated a cancer surveillance programme. CONCLUSIONS: The novel assay is efficient, accurate and has a major impact on patient care.
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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 | 0.030 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".