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Record W2002362182 · doi:10.1002/mus.22007

An interactive voice response diary for patients with non‐dystrophic myotonia

2011· article· en· W2002362182 on OpenAlexaff
Jeffrey Statland, Yunxia Wang, Rachel Richesson, Brian N. Bundy, Laura Herbelin, Joe Gomes, Jaya Trivedi, Shannon L. Venance, Anthony A. Amato, Michael G. Hanna, Robert C. Griggs, Richard J. Barohn

Bibliographic record

VenueMuscle & Nerve · 2011
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsLondon Health Sciences Centre
FundersNational Center for Advancing Translational SciencesNational Center for Research Resources
KeywordsMyotoniaMedicineMexiletineMyotonia congenitaPhysical therapyPhysical medicine and rehabilitationWeaknessMuscle weaknessAudiologyInternal medicineSurgeryMyotonic dystrophy

Abstract

fetched live from OpenAlex

INTRODUCTION: Non-dystrophic myotonia (NDM) is caused by mutations in muscle chloride and sodium channels. Currently, there is no standardized instrument for documenting symptom frequency and severity in NDM. METHODS: Subjects used an automated, interactive, telephone-based voice response diary (IVR) to record frequency and severity of stiffness, weakness, pain, and tiredness once a week for 8 weeks, after their baseline visits. RESULTS: We describe the IVR and report data on 76 subjects for a total of 385 person-weeks. Overall there were 5.1 calls per subject. Forty-eight subjects called in 5 or more times, and 14 called in 8 times. Stiffness was both the most frequent and severe symptom. Warm-up and handgrip myotonia were associated with higher severity scores for stiffness. CONCLUSIONS: IVR is a convenient technology to allow patient reporting of repeated and real-time symptom frequency and severity, and it is presently being used in a trial of mexiletine in NDM.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.027
GPT teacher head0.257
Teacher spread0.231 · 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

Citations29
Published2011
Admission routes1
Has abstractyes

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