Safety and Diagnostic Utility of Brain Biopsy and Metagenomics in Decision-Making for Patients with Inborn Errors of Immunity (IEI) and Unexplained Neurological Manifestations
Bibliographic record
Abstract
Unexplained neurological symptoms can pose a diagnostic challenge in patients with inborn errors of immunity (IEI) where the aetiology can be varied, and diverse pathologies may require contrasting treatments. Brain biopsy, the process of sampling brain tissue directly, has historically provided histological and microbiological information and can now be exploited for deep metagenomic next generation analysis (mNGS). We conducted a retrospective analysis of clinical and diagnostic data on paediatric patients with IEI who had a brain biopsy between 2010 and 2022 at a UK tertiary centre where 14 patients fulfilled our search criteria. We report on clinical characteristics, adverse events and the additional impact of mNGS of brain biopsies, where these were conducted. We found that brain biopsy enabled diagnostics with manageable complications in most cases, either by tissue or metagenomics analysis (n = 11/14, 79%). We found that mNGS analysis improved the diagnostic yield of brain biopsy in 29% of IEI cases (n = 4/14). Brain biopsy enabled a change in management in 71% of cases (n = 10/14). This series provides compelling evidence for the safe and purposeful use of brain biopsy in children with IEI.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".