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Record W2915019711 · doi:10.1002/mus.26420

The myotonic dystrophy experience: a North American cross‐sectional study

2019· article· en· W2915019711 on OpenAlexaff
Katharine A. Hagerman, Sarah J. Howe, Chad Heatwole

Bibliographic record

VenueMuscle & Nerve · 2019
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsThe Marigold Foundation
FundersStanford Bio-X
KeywordsMyotonic dystrophyMedicineCross-sectional studyDiseasePopulationGerontologyPediatricsPhysical therapyInternal medicinePathologyEnvironmental health

Abstract

fetched live from OpenAlex

INTRODUCTION: Myotonic dystrophy (DM) is a chronic, multisystemic, neurological condition. Patients and caregivers are uniquely suited to identify what symptoms are most important and highlight the unmet needs that are most relevant to DM. METHODS: We conducted a North American, cross-sectional study of people with DM type-1, congenital DM, and DM type-2 and their family members. We sent patients and caregivers separate surveys to identify and quantitate the issues of greatest importance, examine the differences between groups, and identify the most important challenges experienced by this population. RESULTS: 1,180 people with DM and 402 family members/caregivers responded to the surveys. They reported considerable physical and cognitive symptoms, extensive diagnostic delays, and varying clinical phenotypes on the basis of DM type. DISCUSSION: Marked disease burden and numerous unmet needs exist in DM. These needs vary based on DM type and highlight the complex clinical phenotypes of these neurological disorders. Muscle Nerve 59:457-464, 2019.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.295
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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

Citations40
Published2019
Admission routes1
Has abstractyes

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