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Techniques, Complications, and Pitfalls of Endoscopic Saphenectomy for Coronary Artery Bypass Grafting Surgery

2005· article· en· W1969223170 on OpenAlexaffabout
Louis P. Perrault, Robert Kollpainter, Pierre Pagé, Ronald G. Miles, Daniel Tanguay, Michel Carrier

Bibliographic record

VenueJournal of Cardiac Surgery · 2005
Typearticle
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsMedicineBypass graftingArterySurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: The purpose of the present paper is to discuss technical features of endoscopic saphenectomy with CO2 insufflation for CABG surgery and to highlight special situations in which to avoid potential pitfalls that may be encountered. METHODS: The initial section describes the approaches used with endoscopic saphenectomy with insufflation of CO2 at the Montreal Heart Institute and the Wausau Heart Institute, which can be used by operators with different levels of experience. The following sections expose numerous intraoperative tricks and maneuvers to facilitate the procedure. Specific situations associated with increased difficulty are reviewed such as the obese patient, venous insufficiency, vein tethered to the dermis, and double venous systems. Complications specific to the technique such as gas embolism, tunnelitis, and hematomas are discussed and preventive measures are proposed to avoid the rare morbidity associated with endoscopic harvesting. Preparation of the patient as well as monitoring during the intervention are also reviewed. RESULTS: Adherence to the comprehensive approach presented in this text should to ensure retrieval of high-quality grafts with a low complication rate providing patients with the full benefits, both cardiac and functional, of this minimally invasive technique of saphenous vein harvesting for 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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.000
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 head0.020
GPT teacher head0.276
Teacher spread0.256 · 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

Citations9
Published2005
Admission routes2
Has abstractyes

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