MétaCan
Menu
Back to cohort
Record W4244210006 · doi:10.1186/s12968-017-0408-9

Correction to: Clinical recommendations for cardiovascular magnetic resonance mapping of T1, T2, T2* and extracellular volume: A consensus statement by the Society for Cardiovascular Magnetic Resonance (SCMR) endorsed by the European Association for Cardiovascular Imaging (EACVI)

2018· erratum· en· W4244210006 on OpenAlexaff
Daniel Messroghli, James Moon, Vanessa M. Ferreira, Lars Grosse‐Wortmann, Taigang He, Peter Kellman, Julia Mascherbauer, Reza Nezafat, Michael Salerno, Erik B. Schelbert, Andrew J. Taylor, Richard B. Thompson, Martin Ugander, Ruud B. van Heeswijk, Matthias G. Friedrich

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2018
Typeerratum
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of CalgaryMcGill UniversityUniversité de MontréalUniversity of AlbertaSickKids FoundationUniversity of TorontoHospital for Sick Children
FundersSiemens HealthineersRosetrees TrustBracco DiagnosticsBritish Heart FoundationNational Institute for Health and Care Research
KeywordsStatement (logic)AngiologyMedicineMagnetic resonance imagingCardiac magnetic resonanceInternal medicineRadiologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Correction to: J Cardiovasc Magn Reson (2017) 19: 75. DOI: 10.1186/s12968-017-0389-8 In the original publication of this article [1] the “Competing interests” section was incorrect. The original publication stated the following competing interests:

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)0.091
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 score0.076
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.091
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0070.013
Insufficient payload (model declined to judge)0.0760.055

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 head0.273
Teacher spread0.254 · 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

Citations58
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cardiovascular Magnetic ResonanceSame topicCardiac Imaging and DiagnosticsFrench-language works237,207