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Record W2057441779 · doi:10.1016/j.media.2014.10.004

Right ventricle segmentation from cardiac MRI: A collation study

2014· article· en· W2057441779 on OpenAlexaff
Caroline Petitjean, María A. Zuluaga, Wenjia Bai, Jean‐Nicolas Dacher, Damien Grosgeorge, Jérôme Caudron, Su Ruan, Ismail Ben Ayed, M. Jorge Cardoso, Hsiang-Chou Chen, Daniel Jiménez‐Carretero, María J. Ledesma‐Carbayo, Christos Davatzikos, Jimit Doshi, Güray Erus, Oskar Maier, Cyrus M. S. Nambakhsh, Yangming Ou, Sébastien Ourselin, Chun-Wei Peng, Nicholas S. Peters, T.M. Peters, Martin Rajchl, Daniel Rueckert, A. Santos, Wenzhe Shi, Ching‐Wei Wang, Haiyan Wang, Jing Yuan

Bibliographic record

VenueMedical Image Analysis · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsWestern UniversityCARE Canada
FundersMedical Research CouncilNational Science CouncilMinisterio de Ciencia e InnovaciónBritish Heart FoundationEngineering and Physical Sciences Research CouncilComunidad de Madrid
KeywordsHausdorff distanceSegmentationComputer scienceArtificial intelligenceTracingMetric (unit)Pattern recognition (psychology)Computer visionVentricleMedicine

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)0.003
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.334
Teacher spread0.329 · 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

Citations242
Published2014
Admission routes1
Has abstractno

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