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Record W4404100133 · doi:10.1016/j.jaccas.2024.102693

In Vivo Optical Coherence Tomography Detection of Repetitive Plaque Erosion Leading to Healed Plaques and Lesion Progression

2024· article· en· W4404100133 on OpenAlexaff
William Gibson, Elie Akl, Elvin Kedhi

Bibliographic record

VenueJACC Case Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsRoyal Victoria Hospital
Fundersnot available
KeywordsOptical coherence tomographyLesionIn vivoMedicineRadiologyPathologyBiology

Abstract

fetched live from OpenAlex

Plaque erosion is the second most common cause of acute coronary syndromes (ACS). Small studies using optical coherence tomography (OCT) have shown favorable outcomes in select patients with plaque erosion treated conservatively without stent implantation. Unlike plaque rupture, the role of plaque erosion in the formation of healed plaques and subsequent flow-limiting coronary stenoses is less certain. We present the case of a medically managed anterior ST-segment elevation myocardial infarction (STEMI) in a 53-year-old man, secondary to plaque erosion in the mid-left anterior descending (LAD) artery. Repeat OCT at 2 weeks demonstrated adequate resolution of intraluminal thrombus along with plaque layering with varying optical densities and negative invasive physiological testing. This case provides unique in vivo evidence of plaque erosion healing leading to the development of further plaque layering. We hypothesize that the multilayered plaque appearance after erosion is representative of repetitive episodes of plaque instability at the same coronary location, which may eventually lead to progression of plaque and reduction of lumen. Finally, there is mounting evidence that healed plaques represent an important predictor of future adverse events, raising important questions regarding the preconceived notion that plaque erosion has a benign course when treated conservatively.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.342
Teacher spread0.321 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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 abstractyes

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