Neurofilament as a potential biomarker for spinal muscular atrophy
Bibliographic record
Abstract
Abstract Objective To evaluate plasma phosphorylated neurofilament heavy chain ( pNF ‐H) as a biomarker in spinal muscular atrophy ( SMA ). Methods Levels of pNF ‐H were measured using the ProteinSimple ® platform in plasma samples from infants with SMA enrolled in ENDEAR ( NCT 02193074) and infants/children without neurological disease. Results Median pNF ‐H plasma level was 167.0 pg/mL (7.46–7,030; n = 34) in children without SMA (aged 7 weeks–18 years) and was higher in those aged < 1 versus 1–18 years ( P = 0.0002). In ENDEAR participants with infantile‐onset SMA , median baseline pNF ‐H level (15,400 pg/mL; 2390–50,100; n = 117) was ~10‐fold higher than that of age‐matched infants without SMA ( P < 0.0001) and ~90‐fold higher than children without SMA ( P < 0.0001). Higher pretreatment pNF ‐H levels in infants with SMA were associated with younger age at symptom onset, diagnosis, and first dose; lower baseline Children's Hospital of Philadelphia Infant Test of Neuromuscular Disorders score; and lower peroneal compound muscle potential amplitude. Nusinersen treatment was associated with a rapid and greater decline in pNF ‐H levels: nusinersen‐treated infants experienced a steep 71.9% decline at 2 months to 90.1% decline at 10 months; sham control–treated infants declined steadily by 16.2% at 2 months and 60.3% at 10 months. Interpretation Plasma pNF ‐H levels are elevated in infants with SMA . Levels inversely correlate with age at first dose and several markers of disease severity. Nusinersen treatment is associated with a significant decline in pNF ‐H levels followed by relative stabilization. Together these data suggest plasma pNF ‐H is a promising marker of disease activity/treatment response in infants with SMA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".