Solving the puzzle of spinal muscular atrophy: What are the missing pieces?
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is an autosomal recessive, lower motor neuron disease. Clinical heterogeneity is pervasive: three infantile (type I-III) and one adult-onset (type IV) forms are recognized. Type I SMA is the most common genetic cause of death in infancy and accounts for about 50% of all patients with SMA. Most forms of SMA are caused by mutations of the survival motor neuron (SMN1) gene. A second gene that is 99% identical to SMN1 (SMN2) is located in the same region. The only functionally relevant difference between the two genes identified to date is a C → T transition in exon 7 of SMN2, which determines an alternative spliced isoform that predominantly excludes exon 7. Thus, SMN2 genes do not produce sufficient full length SMN protein to prevent the onset of the disease. Since the identification of the causative mutation, biomedical research of SMA has progressed by leaps and bounds: from clues on the function of SMN protein, to the development of different models of the disease, to the identification of potential treatments, some of which are currently in human trials. The aim of this review is to elucidate the current state of knowledge, emphasizing how close we are to the solution of the puzzle that is SMA, and, more importantly, to highlight the missing pieces of this puzzle. Filling in these gaps in our knowledge will likely accelerate the development and delivery of efficient treatments for SMA patients and be a prerequisite towards achieving our final goal, the cure of 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".