Baseline results of the Neuro<scp>NEXT</scp> spinal muscular atrophy infant biomarker study
Bibliographic record
Abstract
OBJECTIVE: This study prospectively assessed putative promising biomarkers for use in assessing infants with spinal muscular atrophy (SMA). METHODS: This prospective, multi-center natural history study targeted the enrollment of SMA infants and healthy control infants less than 6 months of age. Recruitment occurred at 14 centers within the NINDS National Network for Excellence in Neuroscience Clinical Trials (NeuroNEXT) Network. Infant motor function scales and putative electrophysiological, protein and molecular biomarkers were assessed at baseline and subsequent visits. RESULTS: Enrollment began November, 2012 and ended September, 2014 with 26 SMA infants and 27 healthy infants enrolled. Baseline demographic characteristics of the SMA and control infant cohorts aligned well. Motor function as assessed by the Test for Infant Motor Performance Items (TIMPSI) and the Children's Hospital of Philadelphia Infant Test of Neuromuscular Disorders (CHOP-INTEND) revealed significant differences between the SMA and control infants at baseline. Ulnar compound muscle action potential amplitude (CMAP) in SMA infants (1.4 ± 2.2 mV) was significantly reduced compared to controls (5.5 ± 2.0 mV). Electrical impedance myography (EIM) high-frequency reactance slope (Ohms/MHz) was significantly higher in SMA infants than controls SMA infants had lower survival motor neuron (SMN) mRNA levels in blood than controls, and several serum protein analytes were altered between cohorts. INTERPRETATION: By the time infants were recruited and presented for the baseline visit, SMA infants had reduced motor function compared to controls. Ulnar CMAP, EIM, blood SMN mRNA levels, and serum protein analytes were able to distinguish between cohorts at the enrollment visit.
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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) | 0.000 | 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 | 0.000 |
| 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".