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Record W2752787259 · doi:10.1002/ajmg.a.38418

Treating pediatric neuromuscular disorders: The future is now

2017· review· en· W2752787259 on OpenAlexaff
James J. Dowling, Hernán Gonorazky, Ronald D. Cohn, Craig Campbell

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsWestern UniversityHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsSpinal muscular atrophyMedicineNeuromuscular diseaseDuchenne muscular dystrophyEtiologyNeuromuscular transmissionDiseaseMuscular dystrophyPhysical medicine and rehabilitationPediatricsPathologyInternal medicine

Abstract

fetched live from OpenAlex

Pediatric neuromuscular diseases encompass all disorders with onset in childhood and where the primary area of pathology is in the peripheral nervous system. These conditions are largely genetic in etiology, and only those with a genetic underpinning will be presented in this review. This includes disorders of the anterior horn cell (e.g., spinal muscular atrophy), peripheral nerve (e.g., Charcot-Marie-Tooth disease), the neuromuscular junction (e.g., congenital myasthenic syndrome), and the muscle (myopathies and muscular dystrophies). Historically, pediatric neuromuscular disorders have uniformly been considered to be without treatment possibilities and to have dire prognoses. This perception has gradually changed, starting in part with the discovery and widespread application of corticosteroids for Duchenne muscular dystrophy. At present, several exciting therapeutic avenues are under investigation for a range of conditions, offering the potential for significant improvements in patient morbidities and mortality and, in some cases, curative intervention. In this review, we will present the current state of treatment for the most common pediatric neuromuscular conditions, and detail the treatment strategies with the greatest potential for helping with these devastating diseases.

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 score0.986
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.347
Teacher spread0.324 · 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

Citations129
Published2017
Admission routes1
Has abstractyes

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