Outcome of two siblings with late-onset Krabbe disease following allogeneic hematopoietic stem cell transplantation: And review of literature
Bibliographic record
Abstract
To compare delayed-onset Krabbe disease (KD) and outcomes between two siblings in relation to allogeneic hematopoietic stem cell transplantation (HSCT). We provide a descriptive report on two siblings with late-onset KD and their clinical course before and after HSCT. The index case presented with neurological symptoms that were presumptively diagnosed with multiple sclerosis (MS). Despite treatment with immunotherapy, the patient continued to decline progressively, prompting reassessment in the neurometabolic clinic 17 years after symptom onset. Symmetrical white matter changes in the pyramidal tract and optic radiation on MRI, absence of GALC enzyme activity in the blood, and identification of a pathogenic and likely pathogenic GALC variants confirmed the diagnosis of late-onset KD. After the proband's diagnosis, late-onset KD was also confirmed in his two siblings. In contrast to the index case, the younger sibling underwent HSCT with milder symptoms, stabilizing neurocognitive status, and imaging findings. Despite advanced disease, the proband's condition has stabilized following HSCT. Late-onset KD is clinically heterogeneous in presentation. Recognizing its progressive course, variable clinical features, positive family history (if present), and characteristic imaging can enable timely recognition. Earlier intervention with HSCT may modify the outcome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".