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Record W4323652592 · doi:10.1007/s00415-023-11633-1

Periostin as a blood biomarker of muscle cell fibrosis, cardiomyopathy and disease severity in myotonic dystrophy type 1

2023· article· en· W4323652592 on OpenAlexafffund
Chi Nguyen, Cecilia Jimenez‐Moreno, Charles Joseph Bowers, Nikoletta Nikolenko, Andreas Hentschel, Thomas Müntefering, Angus Isham, Tobias Ruck, Matthias Vorgerd, Vera Dobelmann, Geneviève Gourdon, Ulrike Schara‐Schmidt, Andrea Gangfuß, Charlotte Schröder, Albert Sickmann, Claudia Groß, Gráinne S. Gorman, Werner Stenzel, Laxmikanth Kollipara, Denisa Hathazi, Sally Spendiff, Cynthia Gagnon, Corinna Preuße, Élise Duchesne, Hanns Lochmüller, Andreas Roos

Bibliographic record

VenueJournal of Neurology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsUniversité du Québec à ChicoutimiUniversité de SherbrookeOttawa HospitalChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersDaiichi Sankyo EuropeAbbott VascularServierCanadian Institutes of Health ResearchDeutsche Gesellschaft für Kardiologie-Herz und Kreislaufforschung.Deutsche HerzstiftungAFM-TéléthonAbiomedVifor PharmaNational Institute for Health Research Health Protection Research UnitReCor MedicalBoston Scientific CorporationEsperion TherapeuticsDeutsche ForschungsgemeinschaftDeutsche Gesellschaft für MuskelkrankeMinisterium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-WestfalenAmgenEdwards LifesciencesCanada Research ChairsAstraZeneca
KeywordsPeriostinMedicineMyotonic dystrophyBiomarkerCardiomyopathyNeurologyFibrosisPathologyInternal medicineDiseaseCardiologyHeart failureBiologyGeneticsExtracellular matrix

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.257
Teacher spread0.237 · 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

Citations11
Published2023
Admission routes2
Has abstractno

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Same venueJournal of NeurologySame topicGenetic Neurodegenerative DiseasesFrench-language works237,207