Natural history of infantile‐onset spinal muscular atrophy
Bibliographic record
Abstract
OBJECTIVE: Infantile-onset spinal muscular atrophy (SMA) is the most common genetic cause of infant mortality, typically resulting in death preceding age 2. Clinical trials in this population require an understanding of disease progression and identification of meaningful biomarkers to hasten therapeutic development and predict outcomes. METHODS: A longitudinal, multicenter, prospective natural history study enrolled 26 SMA infants and 27 control infants aged <6 months. Recruitment occurred at 14 centers over 21 months within the NINDS-sponsored NeuroNEXT (National Network for Excellence in Neuroscience Clinical Trials) Network. Infant motor function scales (Test of Infant Motor Performance Screening Items [TIMPSI], The Children's Hospital of Philadelphia Infant Test for Neuromuscular Disorders, and Alberta Infant Motor Score) and putative physiological and molecular biomarkers were assessed preceding age 6 months and at 6, 9, 12, 18, and 24 months with progression, correlations between motor function and biomarkers, and hazard ratios analyzed. RESULTS: Motor function scores (MFS) and compound muscle action potential (CMAP) decreased rapidly in SMA infants, whereas MFS in all healthy infants rapidly increased. Correlations were identified between TIMPSI and CMAP in SMA infants. TIMPSI at first study visit was associated with risk of combined endpoint of death or permanent invasive ventilation in SMA infants. Post-hoc analysis of survival to combined endpoint in SMA infants with 2 copies of SMN2 indicated a median age of 8 months at death (95% confidence interval, 6, 17). INTERPRETATION: These data of SMA and control outcome measures delineates meaningful change in clinical trials in infantile-onset SMA. The power and utility of NeuroNEXT to provide "real-world," prospective natural history data sets to accelerate public and private drug development programs for rare disease is demonstrated. Ann Neurol 2017;82:883-891.
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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".