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Chronic Total Occlusion Percutaneous Coronary Intervention: Present and Future

2025· review· en· W4409408431 on OpenAlexaff
Emmanouil S. Brilakis, Yader Sandoval, Lorenzo Azzalini, Gregor Leibundgut, Roberto Garbo, Allison B. Hall, Rhian Davies, Kambis Mashayekhi, Masahisa Yamane, Alexandre Avran, Jaikirshan Khatri, Khaldoon Alaswad, Farouc A. Jaffer, Stéphane Rinfret

Bibliographic record

VenueCirculation Cardiovascular Interventions · 2025
Typereview
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicinePercutaneous coronary interventionReentryRadiologyOcclusionPercutaneousSubspecialtyCoronary occlusionCoronary angiographyMedical physicsSurgeryCardiologyMyocardial infarction

Abstract

fetched live from OpenAlex

Chronic total occlusion percutaneous coronary intervention has evolved into a subspecialty of interventional cardiology. Using a variety of antegrade and retrograde techniques, experienced operators currently achieve success rates of 85% to 90%, with an incidence of major periprocedural complications of ≈2% to 3%. Several developments in equipment (new microcatheters and guidewires, novel reentry devices), imaging (computed tomography angiography guidance, intravascular imaging for reentry), techniques (intraocclusion contrast injection, advanced subintimal tracking and reentry), and artificial intelligence (automated computed tomography image analysis and prediction of the likelihood of crossing success with various techniques) could further improve outcomes. Global collaboration and rapid dissemination of new developments accelerate the pace of progress. While innovation is exciting and necessary, adhering to the basic principles of chronic total occlusion percutaneous coronary intervention (such as continual assessment of risks and benefits, meticulous angiographic review, and use of dual injection) remains critical for achieving optimal patient outcomes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.014
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.332
Teacher spread0.301 · 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 designOther design
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

Citations11
Published2025
Admission routes1
Has abstractyes

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