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Record W2974033904 · doi:10.1002/mus.26712

Patient‐reported disease burden in oculopharyngeal muscular dystrophy

2019· article· en· W2974033904 on OpenAlexafffund
Nicolette S. Kurtz, Claudia Côté, Chad Heatwole, Cynthia Gagnon, Sarah Youssof

Bibliographic record

VenueMuscle & Nerve · 2019
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsUniversité de Sherbrooke
FundersFonds de Recherche du Québec - Santé
KeywordsOculopharyngeal muscular dystrophyDysphagiaMedicineDiseaseCoping (psychology)Physical therapyMuscular dystrophyClinical psychologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: There is currently little evidence regarding oculopharyngeal muscular dystrophy (OPMD) disease burden reported by patients. In this study we aim to elicit direct patient input regarding OPMD disease burden. METHODS: We conducted semistructured interviews with 25 participants with genetically confirmed OPMD and a wide range of disease duration (15 ± 8 years). Using the Framework Technique, themes and categories were then extracted. RESULTS: Analyses revealed 7 themes (physical impact, mental impact, social impact, perception of progression, treatment perceptions, coping strategies, and access to disease information), encompassing 27 categories of OPMD disease burden. The most frequent categories were related to dysphagia, coping strategies for dysphagia, and impaired mobility. DISCUSSION: This study demonstrates the importance of considering, when providing clinical care, the broad range of coping strategies patients use to deal with OPMD symptoms, especially dysphagia, to properly assess limitations and monitor real disease progression.

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)0.005
metaresearch head score (Gemma)0.014
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 score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
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 science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.334
Teacher spread0.310 · 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

Citations30
Published2019
Admission routes2
Has abstractyes

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