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Record W3159607530 · doi:10.1097/hco.0000000000000871

Percutaneous management of calcified coronary arteries – review of atherectomy and lithotripsy devices and why it is important

2021· review· en· W3159607530 on OpenAlexaff
Lucas Burke, John Graham

Bibliographic record

VenueCurrent Opinion in Cardiology · 2021
Typereview
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineConventional PCIAtherectomyPercutaneous coronary interventionStentBalloonPercutaneousLithotripsyRevascularizationRadiologyCalcificationClinical PracticeCardiologyRestenosisMyocardial infarction

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Coronary artery calcification (CAC) predisposes to suboptimal revascularization outcomes after percutaneous coronary intervention (PCI). Despite the availability of several plaque modification devices, their rates of use remain low despite the prevalence of CAC encountered in clinical practice. It is important to understand how each device can be utilized in clinical practice in order to improve outcomes after PCI. RECENT FINDINGS: This article summarizes the most recent clinical evidence for each plaque modification device. Although rotational atherectomy is the most frequently used device for plaque modification, the use of orbital atherectomy (OA) has been increasing. Balloon-based strategies including recent studies evaluating a novel intravascular lithotripsy balloon have shed light on the benefits of nonablative devices in modifying CAC during PCI. SUMMARY: CAC poses significant technical challenges in achieving optimal stent results. Several intracoronary plaque modification devices are currently available and understanding the technical aspects, indications and contraindications to the use of each device is essential. Although rotational and OA are most commonly used, laser atherectomy and balloon-based devices may offer an advantage in certain lesion subsets.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.695
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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 head0.111
GPT teacher head0.422
Teacher spread0.311 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2021
Admission routes1
Has abstractyes

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