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Record W4394934707 · doi:10.1016/j.jscai.2024.101934

A Systematic Approach to the Evaluation of the Coronary Microcirculation Using Bolus Thermodilution: CATH CMD

2024· review· en· W4394934707 on OpenAlexaff
Carlos Collet, A. Yong, Daniel Munhoz, Takashi Akasaka, Colin Berry, John E. Blair, Damien Collison, Thomas Engstrøm, Javier Escaned, William F. Fearon, Tommaso Gori, Bon‐Kwon Koo, Adrian F. Low, Steve Miner, M. Ng, Takuya Mizukami, Hiroaki Shimokawa, Nathaniel R. Smilowitz, Nadia R. Sutton, Johan Svanerud, Jennifer A. Tremmel, Takayuki Warisawa, Nick E.J. West, Ziad A. Ali

Bibliographic record

VenueJournal of the Society for Cardiovascular Angiography & Interventions · 2024
Typereview
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsSouthlake Regional Health Center
FundersEngineering and Physical Sciences Research CouncilMedical Research CouncilBritish Heart Foundation
KeywordsMicrocirculationMedicineCardiologyCoronary flow reserveCath labFractional flow reserveCoronary artery diseaseInternal medicineIschemiaAnginaBlood flowCoronary circulationCoronary angiographyMyocardial infarction

Abstract

fetched live from OpenAlex

Coronary microvascular dysfunction (CMD) can cause myocardial ischemia in patients presenting with angina without obstructive coronary artery disease (ANOCA). Evaluating for CMD by using the thermodilution technique offers a widely accessible means of assessing microvascular resistance. Through this technique, 2 validated indices, namely coronary flow reserve and the index of microcirculatory resistance, can be computed, facilitating investigation of the coronary microcirculation. The index of microcirculatory resistance specifically estimates minimum achievable microvascular resistance within the coronary microcirculation. We aim to review the bolus thermodilution method, outlining the fundamental steps for conducting measurements and introducing an algorithmic approach (CATH CMD) to systematically evaluate the coronary microcirculation. Embracing a standardized approach, exemplified by the CATH CMD algorithm, will facilitate adoption of this technique and streamline the diagnosis of CMD.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.384
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.386
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.122
GPT teacher head0.379
Teacher spread0.257 · 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 designMeta-analysis
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

Citations17
Published2024
Admission routes1
Has abstractyes

Explore more

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