MétaCan
Menu
Back to cohort
Record W4400066325 · doi:10.1016/j.jcct.2024.05.029

Deep Learning-Based Novel Coronary Artery Calcium Dispersion And Density (CAC-DAD) Score For Predicting Myocardial Infarction And Cardiovascular Death

2024· article· en· W4400066325 on OpenAlexaff
G. Huangfu, G. Dwivedi, A. Ihdayhid, Simon C.M. Kwok, J. Konstantopoulos, K. Niu, Juan Lü, Gemma A. Figtree, Christopher W.K. Chow, L. Dembo, B. Adler, Christian Hamilton‐Craig, Matthew T.V. Chan, Craig Butler, Vikas Tandon, Peter Nägele, Pamela K. Woodard, Marko Mrkobrada, Wojciech Szczeklik, Y. Aziz, Bruce Biccard, P.J. Devereaux, T. Sheth, B. Chow

Bibliographic record

VenueJournal of cardiovascular computed tomography · 2024
Typearticle
Languageen
FieldHealth Professions
TopicArtificial Intelligence in Healthcare
Canadian institutionsUniversity of OttawaWestern UniversityMcMaster UniversityUniversity of Alberta
Fundersnot available
KeywordsMedicineCoronary artery calciumCardiologyInternal medicineMyocardial infarctionCoronary artery disease

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.335
Teacher spread0.275 · 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 teacher head, not a consensus.

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

Citations0
Published2024
Admission routes1
Has abstractno

Explore more

Same venueJournal of cardiovascular computed tomographySame topicArtificial Intelligence in HealthcareFrench-language works237,207