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Record W2003590746 · doi:10.1186/1532-429x-15-105

Evaluation of current algorithms for segmentation of scar tissue from late Gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge

2013· article· en· W2003590746 on OpenAlexaff
Rashed Karim, R. James Housden, Mayuragoban Balasubramaniam, Zhong Chen, Daniel J. Perry, Ayesha Uddin, Yosra Al-Beyatti, Ebrahim Palkhi, Prince Acheampong, Samantha Obom, Anja Hennemuth, Yingli Lu, Wenjia Bai, Wenzhe Shi, Yi Gao, Heinz‐Otto Peitgen, Perry Radau, Reza Razavi, Allen Tannenbaum, Daniel Rueckert, Joshua Cates, Tobias Schaeffter, Dana C. Peters, Rob MacLeod, Kawal Rhode

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2013
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
FundersNational Institute for Health and Care ResearchEngineering and Physical Sciences Research CouncilKing's College LondonNational Institute of General Medical SciencesMedical Research CouncilWellcome Trust
KeywordsMedicineAngiologyMagnetic resonance imagingGadoliniumSegmentationCardiac magnetic resonanceLeft atriumRadiologyAlgorithmInternal medicineArtificial intelligenceComputer scienceAtrial fibrillation

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.030
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.044
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.003
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0060.003
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.117
GPT teacher head0.386
Teacher spread0.268 · 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 designSimulation or modeling
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

Citations178
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cardiovascular Magnetic Resonance→Same topicAtrial Fibrillation Management and Outcomes→French-language works237,207→