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Record W4388312301 · doi:10.1016/j.nmd.2023.10.020

The Dutch registry for facioscapulohumeral muscular dystrophy: Cohort profile and longitudinal patient reported outcomes

2023· article· en· W4388312301 on OpenAlexaboutno aff
Joost Kools, Johanna C.W. Deenen, Anna M. Blokhuis, André L. M. Verbeek, Nicol C. Voermans, Baziel G.M. van Engelen

Bibliographic record

VenueNeuromuscular Disorders · 2023
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsnot available
FundersRadboud Universitair Medisch Centrum
KeywordsMedicineFacioscapulohumeral muscular dystrophyPhysical therapyCohortQuality of life (healthcare)WeaknessMuscular dystrophyDepression (economics)Cohort studyMuscle weaknessInternal medicineSurgery

Abstract

fetched live from OpenAlex

Facioscapulohumeral dystrophy (FSHD) is the second most prevalent inherited muscular disorder and currently lacks a pharmaceutical treatment. The Dutch FSHD Registry was initiated in 2015 as a result of an international collaboration on trial readiness. This paper presents the cohort profile and six years of follow-up data of the registered FSHD patients. At the time of self-registration and every six months thereafter, participants were invited to complete a digital survey of patient and disease characteristics and the Dutch versions of the Checklist Individual Strength (CIS20R), the Individualised Neuromuscular Quality of Life Questionnaire (INQoL), the Beck Depression Index - Primary Care and the McGill Pain Questionnaire. From March 2015 to March 2021, 373 participants completed at least one survey. At baseline, fatigue and muscle weakness were the most frequently reported symptoms (median CIS20R sumscore 77 [IQR 60-92], median INQoL Fatigue score 58 [IQR 42-68] and median INQoL weakness score 58 [IQR 42-68]). Pain was experienced most often in the head and shoulder region (193, 52%). Nineteen of the 23 (sub)sections of questionnaires showed no significant changes over time. We conclude that the Dutch FSHD Registry was successfully set up, enabling collection of longitudinal data and facilitating recruitment in several studies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.017
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

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

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 head0.019
GPT teacher head0.292
Teacher spread0.273 · 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 teacher head, 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

Citations14
Published2023
Admission routes1
Has abstractyes

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