Nusinersen treatment of spinal muscular atrophy: current knowledge and existing gaps
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is a recessive disorder caused by a mutation in the survival motor neuron 1 gene (SMN1); it affects 1 in 11 000 newborn infants. The most severe and most common form, type 1 SMA, is associated with early mortality in most cases and severe disability in survivors. Nusinersen, an antisense oligonucleotide, promotes production of full-length protein from the pseudogene SMN2. Nusinersen treatment prolongs survival of patients with type 1 SMA and allows motor milestone acquisition. Patients with type 2 SMA also show progress on different motor scales after nusinersen treatment. Nusinersen was recently approved by the European Medicines Agency and the US Food and Drug Administration; it is now reimbursed in several European countries and in the USA. In Australia, the transition from expanded access programme to commercial availability is coming soon. In New Zealand, an expanded access programme is opened, and in Canada price negotiation for the treatment is in progress. In this review we exemplify the clinical benefit of nusinersen in subgroups of patients with SMA. Nusinersen represents the first efficacious marked approved drug in type 1 and type 2 SMA. Different knowledge gaps, such as results in older patients, in patients with permanent ventilation, in patients with neonatal forms, or in patients after spinal fusion, still need to be addressed. WHAT THIS PAPER ADDS: Identifies gaps in knowledge about the efficacy of nusinersen in broader populations of patients with spinal muscular atrophy. Identifies open questions in populations of patients where proof of efficacy is available.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".