Opening the window: The case for carrier and perinatal screening for spinal muscular atrophy
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is the most common genetically inherited neurodegenerative disease that leads to infant mortality worldwide. SMA is caused by genetic deletion or mutation in the survival of motor neuron 1 (SMN1) gene, which results in a deficiency in SMN protein. For reasons that are still unclear, SMN protein deficiency predominantly affects α-motor neurons, resulting in their degeneration and subsequent paralysis of limb and trunk muscles, progressing to death in severe cases. Emerging evidence suggests that SMN protein deficiency also affects the heart, autonomic nervous system, skeletal muscle, liver, pancreas and perhaps many other organs. Currently, there is no cure for SMA. Patient treatment includes respiratory care, physiotherapy, and nutritional management, which can somewhat ameliorate disease symptoms and increase life span. Fortunately, several novel therapies have advanced to human clinical trials. However, data from studies in animal models of SMA indicate that the greatest therapeutic benefit is achieved through initiating treatment as early as possible, before widespread loss of motor neurons has occurred. In this review, we discuss the merit of carrier and perinatal patient screening for SMA considering the efficacy of emerging therapeutics and the physical, emotional and financial burden of the disease on affected families and society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".