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Record W3156066717 · doi:10.1017/cjn.2021.77

Responsiveness of Daytime Sleepiness and Fatigue Scales in Myotonic Dystrophy Type 1

2021· article· en· W3156066717 on OpenAlexafffundvenue
Luc Laberge, Benjamin Gallais, Julie Auclair, Yves Dauvilliers, Isabelle Côté, Jean Mathieu, Cynthia Gagnon

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2021
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité de SherbrookeCégep de JonquièreUniversité du Québec à Chicoutimi
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchMuscular Dystrophy Canada
KeywordsExcessive daytime sleepinessEpworth Sleepiness ScaleDaytimePhysical therapyMyotonic dystrophyContext (archaeology)Physical medicine and rehabilitationPsychologyMedicinePopulationAudiologyPsychiatryInternal medicineSleep disorderCognitionElectroencephalographyPolysomnography

Abstract

fetched live from OpenAlex

Daytime sleepiness and fatigue are prominent symptoms of myotonic dystrophy type 1 (DM1) that can be amenable to treatment in the context of randomized controlled trials. No study has yet documented whether self-reported measures of daytime sleepiness and fatigue can detect change over time and the meaning of this change. The aim was to explore indicators of responsiveness to change and interpretability for the Daytime Sleepiness Scale and the Fatigue Severity Scale in 115 DM1 prospectively followed patients. Results suggest that these two self-reported questionnaires are sufficiently sensitive to detect changes beyond expected measurement error over time in this population.

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)0.010
metaresearch head score (Gemma)0.027
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 score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.054
GPT teacher head0.293
Teacher spread0.239 · 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

Citations4
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques→Same topicGenetic Neurodegenerative Diseases→French-language works237,207→