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Record W2895884786 · doi:10.1080/21678707.2018.1536542

Advances in emerging therapeutics for oculopharyngeal muscular dystrophy

2018· article· en· W2895884786 on OpenAlexaboutno aff
Pradeep Harish, George Dickson, Alberto Malerba

Bibliographic record

VenueExpert Opinion on Orphan Drugs · 2018
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOculopharyngeal muscular dystrophyMuscular dystrophyInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Oculopharyngeal muscular dystrophy (OPMD) is a rare genetic disease affecting approximately 1:100,000 people in Europe but has a significantly higher incidence in some countries such as Canada, Mexico, and among the Bukharan Jews, due to original founder effects. No cure is available for OPMD, and only surgical intervention provides temporary alleviation from the symptoms of the disease.Areas covered: Here, authors discuss the most promising emerging genetic and pharmaceutical therapeutics for OPMD currently in preclinical and clinical development.Expert opinion: At present, some very promising pharmacological treatments targeting aggregates formation are in preclinical development for OPMD with systemic delivery of trehalose currently in clinical trial. While reducing intranuclear inclusions has great potential on attenuating the symptoms, the genetic defect causing the disease is not directly targeted. The development of a gene therapy approach for OPMD based on local intramuscular delivery of adeno-associated viral vectors will hopefully provide significant safe and long-term improvement of the disease.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.037
GPT teacher head0.350
Teacher spread0.313 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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