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Record W4225011333 · doi:10.1186/s12968-021-00827-z

Society for Cardiovascular Magnetic Resonance (SCMR) guidelines for reporting cardiovascular magnetic resonance examinations

2022· article· en· W4225011333 on OpenAlexafffund
W. Gregory Hundley, David A. Bluemke, Jan Bogaert, Scott D. Flamm, Marianna Fontana, Matthias G. Friedrich, Lars Grosse‐Wortmann, Theodoros D. Karamitsos, Christopher M. Kramer, Raymond Y. Kwong, Michael V. McConnell, Eike Nagel, Stefan Neubauer, Robin Nijveldt, Dudley J. Pennell, Steffen E. Petersen, Subha V. Raman, Albert C. van Rossum

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoMcGill University
FundersNational Institutes of HealthNational Institute for Health and Care ResearchBritish Heart FoundationMcGill University
KeywordsMedicineAngiologyMagnetic resonance imagingCardiac magnetic resonanceMedical physicsNuclear magnetic resonanceCardiologyRadiology

Abstract

fetched live from OpenAlex

sponsorship: MF: Salary is from the British Heart Foundation. SF: None. RN: Unrelated research grant from Philips Volcano and Biotronik. SEP acknowledges support from the Barts NIHR Biomedical Research Centre. DJP, AvR, JB, MM, DB, RYK, CMK, TK, SVR, EN, LG-W: None related. SN acknowledges funding from Oxford NIHR Biomedical Research Centre and the British Heart Foundation Centre of Research Excellence. MF acknowledges funding from McGill University. WGH: approximately $10m in Grant support from the NIH. (Barts NIHR Biomedical Research Centre, Oxford NIHR Biomedical Research Centre, British Heart Foundation Centre of Research Excellence, McGill University, NIH)

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)0.019
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.079
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0140.008
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0030.003
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0430.040

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 head0.044
GPT teacher head0.300
Teacher spread0.256 · 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.

Study designNot applicable
DomainReporting
GenreMethods

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

Citations86
Published2022
Admission routes2
Has abstractyes

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