Peripheral neuropathy in Krabbe disease
Bibliographic record
Abstract
BACKGROUND: Hematopoietic stem cell transplantation (HSCT) may slow the progression of Krabbe disease (KD) if performed early in the disease. The authors' studies indicate that more than 90% of patients with KD have severe abnormalities in peripheral nerve conduction. OBJECTIVE: To assess the effect of HSCT on nerve conduction in patients with KD. METHODS: The authors performed serial nerve conduction studies (NCS) in 12 patients with KD after HSCT. The average follow-up was 18 months (6 months to 3 years) after HSCT. Pretransplant NCS were not available in two patients; all others (10 of 12) had significant pretransplant abnormalities. RESULTS: After HSCT, previously absent F-waves (1 patient) and sural sensory responses (SNR) (3 patients) were found recordable. All patients continued to have recordable SNR after HSCT, and these became normal in 7 of 12 patients. Distal motor nerve latency became normal in 6 of 17 and motor nerve conduction velocity (CV) in 2 of 17 nerves; F-wave latencies (FWL) improved in 6 of 17 nerves, but did not become normal in any. There was greater improvement in nerve conduction abnormalities if the transplant was performed earlier in life. After an initial improvement, there was subsequent worsening of motor latencies (2 of 12), motor CV (2 of 12), FWL (3 of 12), and SSR (1 of 12), indicating that benefit from HSCT may be temporary. CONCLUSIONS: Serial nerve conduction studies are useful in following the course of peripheral neuropathy in Krabbe disease. Hematopoietic stem cell transplantation is followed by improvement in peripheral nerve conduction abnormalities in these patients, suggesting remyelination of the nerves.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".