MétaCan
Menu
Back to cohort
Record W4353086291 · doi:10.54097/hset.v36i.6271

Current Development in Treatment of Spinal Muscular Atrophy

2023· article· en· W4353086291 on OpenAlexaff
Xinran Tian

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2023
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpinal muscular atrophySMN1SMA*MedicineDiseaseMotor neuronNeuromuscular diseasePhysical medicine and rehabilitationBioinformaticsPathology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.300
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueHighlights in Science Engineering and TechnologySame topicNeurogenetic and Muscular Disorders ResearchFrench-language works237,207