Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is an autosomal recessive genetic disease that affects the alpha motor units and leads to muscular atrophy. The survival motor neuron-1 (SMN1) mutation, which is found on chromosome 5q13, is responsible for the majority of SMA cases. SMA spectrum range from type 0 to type 4, with various severity, longevity, and symptoms. Usually, the more early onset of the disease is, the more severe the symptoms are. Therefore, early detection is vital since treatment can be implemented as soon as possible. Currently, diagnosis methods include newborn screening, point mutation testing, diagnostic testing, and carrier testing, each with a different purpose. Although there has been no cure for SMA so far, some medications could help to relieve the symptoms and allow patients to achieve a higher quality of life, like Onasemnogene abeparvovec-xioi, Nusinersen, and Evrysdi approved by the FDA. However, these treatments are costly. Additional support for SMA patients comes from physical therapy and careful daily management. Current research aims to identify more biomarkers of SMA to maximize therapeutic success and provide more precise therapeutic doses. This review provides a literature review of the pathogenesis, testing, and treatment of spinal muscular atrophy.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".