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Record W4381149358 · doi:10.3390/pharmaceutics15061764

Expanding the Availability of Onasemnogene Abeparvovec to Older Patients: The Evolving Treatment Landscape for Spinal Muscular Atrophy

2023· review· en· W4381149358 on OpenAlexafffund
Charlotte A. René, Robin J. Parks

Bibliographic record

VenuePharmaceutics · 2023
Typereview
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCure SMA
KeywordsSMN1Spinal muscular atrophySMA*MedicineSpinal cordMotor neuronAtrophyDiseaseCentral nervous systemMuscle atrophyNeuromuscular diseaseInternal medicineBioinformaticsBiology

Abstract

fetched live from OpenAlex

) gene, which leads to a reduced level in the SMN protein within cells. Patients with SMA suffer from a loss of alpha motor neurons in the spinal cord leading to skeletal muscle atrophy in addition to deficits in other tissues and organs. Patients with severe forms of the disease require ventilator assistance and typically succumb to the disease due to respiratory failure. Onasemnogene abeparvovec is an adeno-associated virus (AAV)-based gene therapeutic that has been approved for infants and young children with SMA, and it is delivered through intravenous administration using a dose based on the weight of the patient. While excellent outcomes have been observed in treated patients, the greater viral dose necessary to treat older children and adults raises legitimate safety concerns. Recently, onasemnogene abeparvovec use was investigated in older children through a fixed dose and intrathecal administration, a route that provides a more direct delivery to affected cells in the spinal cord and central nervous system. The promising results observed in the STRONG trial may support approval of onasemnogene abeparvovec for a greater proportion of patients with SMA.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.182
GPT teacher head0.474
Teacher spread0.292 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2023
Admission routes2
Has abstractyes

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