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Record W2144274422 · doi:10.1177/0883073807305665

Perspectives on Clinical Trials in Spinal Muscular Atrophy

2007· review· en· W2144274422 on OpenAlexaff
Kathryn J. Swoboda, John T. Kissel, Thomas O. Crawford, Mark B. Bromberg, Gyula Acsádi, Guy D’Anjou, Kristin J. Krosschell, Sandra P. Reyna, Mary Schroth, Charles Scott, Louise R. Simard

Bibliographic record

VenueJournal of Child Neurology · 2007
Typereview
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsUniversité de MontréalUniversity of ManitobaCentre Hospitalier Universitaire Sainte-Justine
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentAmerican Academy of NeurologyFamilies of Spinal Muscular Atrophy
KeywordsSpinal muscular atrophyClinical trialMedicinePhysical medicine and rehabilitationAtrophyDiseasePhysical therapyPathology

Abstract

fetched live from OpenAlex

Spinal muscular atrophy is one of the most heterogeneous of the single-gene neuromuscular disorders. The broad spectrum of severity, with onset from the prenatal period to adulthood, presents unique challenges in the design and implementation of clinical trials. The clinical classification of subjects into severe (type 1), intermediate (type 2), and mild (type 3) subtypes has proved useful both in enhancing communication among clinicians internationally and in forging the collaborative development of outcome measures for clinical trials. Ideally, clinical trial design in spinal muscular atrophy must take into account the spinal muscular atrophy type, patient age, severity-of-affection status, nature of the therapeutic approach, timing of the proposed intervention relative to disease progression, and relative homogeneity of the cohort to be studied. Following is an overview of the challenges and opportunities, current and future therapeutic strategies, and progress to date in clinical trials in 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)0.010
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.005
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 head0.276
GPT teacher head0.554
Teacher spread0.278 · 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.

Study designOther design
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

Citations70
Published2007
Admission routes1
Has abstractyes

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