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Record W20953384

A new and simplified method for coronary and graft imaging during CABG.

2002· article· en· W20953384 on OpenAlexaff
Fraser D. Rubens, Marc Ruel, Stephen E. Fremes

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineIndocyanine greenCannulaCatheterModality (human–computer interaction)Cardiopulmonary bypassPercutaneousSurgeryRadiologyCardiology
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Improvements in percutaneous catheter interventions and new technical demands in the practice of coronary surgery have increased the need for an accurate and easy-to-use imaging modality for validating the quality of bypass grafts in the operating room. This report examines the initial clinical use of fluorescent cardiac imaging, a technology that uses indocyanine green (ICG) with a portable imaging device to visualize coronary anatomy and grafts intraoperatively. METHODS: The modality was evaluated at two institutions in 20 patients undergoing non-emergent CABG or MIDCAB with respect to safety, feasibility of use, and image quality. Images were generated and acquired with a portable laser diode/infrared camera device after injection of 0.5 ml of ICG (0.5-5.0 mg/ml) either intravenously, via the antegrade cardioplegia cannula, or via the cardiopulmonary bypass circuit. RESULTS: There were no ICG- or imaging device-related complications. The technology was easy-to-use during conventional CABG as well as MIDCAB and adequately demonstrated coronary anatomy, filling of the grafts, and graft patency in all but two patients. In one patient, the use of the modality resulted in the intraoperative recognition and revision of a non-functioning graft. CONCLUSION: This technology is user-friendly in the operating room, appears to be safe, provides good-quality images of coronary anatomy and grafts, and holds promise as an intraoperative graft validation tool for conventional and minimally invasive CABG.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.425

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 head0.022
GPT teacher head0.254
Teacher spread0.233 · 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 designObservational
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

Citations107
Published2002
Admission routes1
Has abstractyes

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