A longitudinal and cross‐sectional study of plasma neurofilament light chain concentration in <scp>Charcot‐Marie‐Tooth</scp> disease
Bibliographic record
Abstract
Abstract Advances in genetic technology and small molecule drug development have paved the way for clinical trials in Charcot‐Marie‐Tooth disease (CMT); however, the current FDA‐approved clinical trial outcome measures are insensitive to detect a meaningful clinical response. There is, therefore, a need to identify sensitive outcome measures or clinically relevant biomarkers. The aim of this study was to further evaluate plasma neurofilament light chain (NFL) as a disease biomarker in CMT. Plasma NFL was measured using SIMOA technology in both a cross‐sectional study of a US cohort of CMT patients and longitudinally over 6 years in a UK CMT cohort. In addition, plasma NFL was measured longitudinally in two mouse models of CMT2D. Plasma concentrations of NFL were increased in a US cohort of patients with CMT1B, CMT1X and CMT2A but not CMT2E compared with controls. In a separate UK cohort, over a 6‐year interval, there was no significant change in plasma NFL concentration in CMT1A or HSN1, but a small but significant reduction in patients with CMT1X. Plasma NFL was increased in wild type compared to GARSC201R mice. There was no significant difference in plasma NFL in GARSP278KY compared to wild type mice. In patients with CMT1A, the small difference in cross‐sectional NFL concentration vs healthy controls and the lack of change over time suggests that plasma NFL may lack sufficient sensitivity to detect a clinically meaningful treatment response in adulthood.
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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 | 0.001 |
| 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 | 0.000 | 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".