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Record W2961044411 · doi:10.1002/mus.26632

Patient‐reported study of the impact of pediatric‐onset myotonic dystrophy

2019· article· en· W2961044411 on OpenAlexaffabout
Michael Hunter, Anne‐Berit Ekström, Craig Campbell, Man Hung, Jerry Bounsanga, Kameron Bates, Heather Adams, Elizabeth Luebbe, Richard T. Moxley, Chad Heatwole, Nicholas E. Johnson

Bibliographic record

VenueMuscle & Nerve · 2019
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsLondon Health Sciences CentreWestern University
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of HealthValerion TherapeuticsNational Institute of Neurological Disorders and StrokeCytokineticsBiogenRegeneron PharmaceuticalsMyotonic Dystrophy Foundation
KeywordsMyotonic dystrophyMedicinePediatricsDiseasePhysical therapyMyotoniaMuscular dystrophyGerontologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: The prevalence and impact of symptoms affecting individuals with pediatric forms of myotonic dystrophy type-1 (DM1) are not well understood. METHODS: Patients from the United States, Canada, and Sweden completed a survey that investigated 20 themes associated with pediatric-onset DM1. Participants reported the prevalence and importance of each theme affecting their lives. Surveys from participants were matched with surveys from their caregivers for additional analysis. RESULTS: The most prevalent symptomatic themes included problems with hands or fingers (79%) and gastrointestinal issues (75%). Problems with urinary/bowel control and gastrointestinal issues were reported to have the greatest impact on patients' lives. Responses from participants and their caregivers had varying levels of agreement among symptomatic themes. DISCUSSION: Many symptoms have meaningful impact on disease burden. The highest levels of agreement between caregivers and individuals with pediatric forms of myotonic dystrophy were found for physical activity themes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.277
Teacher spread0.249 · 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 designObservational
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

Citations13
Published2019
Admission routes2
Has abstractyes

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