Clinical exome sequencing data from patients with inborn errors of immunity: cohort level meta-analysis and the benefit of systematic reanalysis
Bibliographic record
Abstract
Abstract While next generation sequencing has expanded the scientific understanding of Inborn Errors of Immunity (IEI), the clinical use of exome sequencing is still emerging. We performed a cohort level meta-analysis by revisiting clinical exome data from 1,300 IEI patients using an updated in-silico gene panel for IEI. Variants were classified and curated through expert review. The molecular diagnostic yield after standard exome analysis was 11.8%. A systematic reanalysis resulted in the identification of variants of interest in 5.2% of undiagnosed patients, of which 75.4% were (candidate) disease-causing, increasing the molecular diagnostic yield to 15.2%. We find a high degree of actionability in IEI patients with a genetic diagnosis (76.4%). Despite the modest absolute diagnostic gain, these data support the benefit of iterative exome reanalysis in patients with IEI conveying the notion that our current understanding of genes and variants involved in IEI is by far not saturated.
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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 | 0.044 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.019 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".