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Record W2183439828 · doi:10.1093/ehjci/jev307

Cardiac CT for the detection of vulnerable plaque

2015· letter· en· W2183439828 on OpenAlexaff
Filippo Cademartiri, Erica Maffei

Bibliographic record

VenueEuropean Heart Journal - Cardiovascular Imaging · 2015
Typeletter
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMedicineCalcificationIntravascular ultrasoundSign (mathematics)Vulnerability (computing)Vulnerable plaqueRadiologyLimitingArtificial intelligenceComputer scienceCardiologyMathematicsEngineeringComputer security

Abstract

fetched live from OpenAlex

Cardiac computed tomography (CCT) can be used in acute chest pain settings and can identify some features of plaque vulnerability.1–6 The ones that can be considered somehow reliable to date are low or no calcification of the plaque, a large focal plaque burden that translates into the concept of positive remodelling, low density of the non-calcific component, and the ‘napkin ring’ sign.1–6 The last two features are more difficult to reproduce and to address. The concept of plaque disruption is a specific one; something that has to do with a certain imaging pattern where a rupture, a fissure, an intimal tear is visible. It relates with the ‘napkin ring’ sign since it entails that the contrast material penetrates into the layers of the plaque. Off course, intracoronary imaging would be the best approach. Several issues are present when dealing with coronary plaque imaging using CCT.7–10 We can mention the main ones: any density/attenuation quantification is affected by intravascular attenuation (limiting the impact of absolute value quantification); convolution kernel filtering and iterative reconstruction algorithm …

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 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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.487
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.005
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
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 head0.033
GPT teacher head0.274
Teacher spread0.240 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations5
Published2015
Admission routes1
Has abstractyes

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