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Record W4386019396 · doi:10.1007/s00415-023-11862-4

Analysis of muscle magnetic resonance imaging of a large cohort of patient with VCP-mediated disease reveals characteristic features useful for diagnosis

2023· article· en· W4386019396 on OpenAlexaff
Diana Esteller, Marianela Schiava, José Verdú-Díaz, Rocío‐Nur Villar‐Quiles, Boris Dibowski, Nadia Venturelli, Pascal Laforêt, Jorge Alonso‐Pérez, Montse Olivé, Cristina Domínguez‐González, Carmen Paradas, Beatriz Gómez, Anna Kostera‐Pruszczyk, Biruta Kierdaszuk, Carmelo Rodolico, Kristl G. Claeys, Endre Pál, Edoardo Malfatti, Sarah Souvannanorath, Alicia Alonso‐Jiménez, Willem De Ridder, Eline De Smet, George K. Papadimas, C Papadopoulos, Sophia Xirou, Sushan Luo, Nuria Muelas, Juan J. Vílchez, Alba Ramos‐Fransí, Mauro Monforte, Giorgio Tasca, Bjarne Udd, Johanna Palmio, Srtuhi Sri, Sabine Krause, Benedikt Schoser, Roberto Fernández‐Torrón, Adolfo López de Munaín, Elena Pegoraro, Maria Elena Farrugia, Mathias Vorgerd, Georgious Manousakis, Jean‐Baptiste Chanson, Aleksandra Nadaj-Pakleza, Hakan Çetin, Umesh A. Badrising, Jodi Warman‐Chardon, Jorge A. Bevilacqua, Nicholas Earle, Mario Campero, Jorge Díaz, Chiseko Ikenaga, Thomas E. Lloyd, Ichizo Nishino, Yukako Nishimori, Yoshihiko Saito, Yasushi Oya, Yoshiaki Takahashi, Atsuko Nishikawa, Ryo Sasaki, C. Marini-Bettolo, Michela Guglieri, Volker Straub, Tanya Stojkovic, Robert Carlier, Jordi Díaz‐Manera

Bibliographic record

VenueJournal of Neurology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicHereditary Neurological Disorders
Canadian institutionsOttawa Hospital
FundersMuscular Dystrophy UKNational Center of Neurology and PsychiatryMedical Research CouncilAFM-TéléthonNational Institute for Health and Care ResearchAcademy of Medical Sciences
KeywordsMagnetic resonance imagingNeuroradiologyNeurologyMedicineDiseaseCohortMuscle diseaseRadiologyPathologyNuclear magnetic resonancePhysicsPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The diagnosis of patients with mutations in the VCP gene can be complicated due to their broad phenotypic spectrum including myopathy, motor neuron disease and peripheral neuropathy. Muscle MRI guides the diagnosis in neuromuscular diseases (NMDs); however, comprehensive muscle MRI features for VCP patients have not been reported so far. METHODS: We collected muscle MRIs of 80 of the 255 patients who participated in the "VCP International Study" and reviewed the T1-weighted (T1w) and short tau inversion recovery (STIR) sequences. We identified a series of potential diagnostic MRI based characteristics useful for the diagnosis of VCP disease and validated them in 1089 MRIs from patients with other genetically confirmed NMDs. RESULTS: Fat replacement of at least one muscle was identified in all symptomatic patients. The most common finding was the existence of patchy areas of fat replacement. Although there was a wide variability of muscles affected, we observed a common pattern characterized by the involvement of periscapular, paraspinal, gluteal and quadriceps muscles. STIR signal was enhanced in 67% of the patients, either in the muscle itself or in the surrounding fascia. We identified 10 diagnostic characteristics based on the pattern identified that allowed us to distinguish VCP disease from other neuromuscular diseases with high accuracy. CONCLUSIONS: Patients with mutations in the VCP gene had common features on muscle MRI that are helpful for diagnosis purposes, including the presence of patchy fat replacement and a prominent involvement of the periscapular, paraspinal, abdominal and thigh muscles.

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)0.005
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 score0.029
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.259
Teacher spread0.244 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

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