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Record W1507495671 · doi:10.1002/mus.23478

Fatigue and daytime sleepiness scale in myotonic dystrophy type 1

2012· article· en· W1507495671 on OpenAlexaff
Mieke C. E. Hermans, Ingemar S.J. Merkies, Luc Laberge, Eveline W. Blom, Alan Tennant, Catharina G. Faber

Bibliographic record

VenueMuscle & Nerve · 2012
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsCégep de JonquièreUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsRasch modelEpworth Sleepiness ScaleExcessive daytime sleepinessPsychologyRating scaleMyotonic dystrophyConstruct validityPsychometricsPhysical therapyDaytimePhysical medicine and rehabilitationClinical psychologyMedicineDevelopmental psychologyPsychiatryInternal medicineCognitionPolysomnographySleep disorder

Abstract

fetched live from OpenAlex

INTRODUCTION: Fatigue and excessive daytime sleepiness are frequent complaints in myotonic dystrophy type 1 (DM1) that often overlap. We aimed to construct a combined fatigue and daytime sleepiness rating scale for DM1 using the Rasch measurement model. METHODS: Questionnaires, including the Epworth sleepiness scale, fatigue severity scale, and daytime sleepiness scale, were completed by 354 patients. Data were subjected to Rasch analyses and tested for required measurement issues such as appropriate response categories, absence of item bias, local independence, and unidimensionality. RESULTS: The initial 22 items did not meet Rasch model expectations. After rescoring and removing misfitting items, the final 12-item scale showed good model fit and unidimensionality. High internal consistency (person separation index = 0.80) and validity were demonstrated. CONCLUSIONS: The Rasch-built Fatigue and Daytime Sleepiness Scale, developed specifically for DM1 patients, provides interval measures on a single continuum. Its use is suggested for future clinical trials and therapeutic follow-up.

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)0.003
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.046
GPT teacher head0.280
Teacher spread0.235 · 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

Citations62
Published2012
Admission routes1
Has abstractyes

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