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Record W4391778970 · doi:10.1007/s00415-023-12178-z

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

2024· erratum· en· W4391778970 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, Constantinos 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, Chiara Marini‐Bettolo, Michela Guglieri, Volker Straub, Tanya Stojkovic, Robert Carlier, Jordi Díaz‐Manera

Bibliographic record

VenueJournal of Neurology · 2024
Typeerratum
Languageen
FieldMedicine
TopicCardiac tumors and thrombi
Canadian institutionsOttawa Hospital
FundersNational Institute for Health and Care Research
KeywordsMagnetic resonance imagingNeuroradiologyNeurologyMedicineMuscle diseaseDiseaseCohortRadiologyNuclear magnetic resonanceNeurosciencePathologyPsychologyPhysicsPsychiatry

Abstract

fetched live from OpenAlex

Neurological Motor Disorders

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)0.002
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.044
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0040.002
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0580.029

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 head0.005
GPT teacher head0.254
Teacher spread0.249 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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