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Initial Experience with a New Right Ventricular Support Device for Beating Heart Surgery

2004· article· en· W2024534719 on OpenAlexaboutno aff
Ferdinand Kuhn‐Régnier, H. Geißler, Aurelia J Lercher, Uwe Mehlhorn, Oliver Mehler, Jürgen H Fischer, E. R. de Vivie

Bibliographic record

VenueArtificial Organs · 2004
Typearticle
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMedicinePerioperativeEjection fractionCardiologyMyocardial infarctionInternal medicineCentral venous pressureHemodynamicsCardiac surgeryArterySurgeryHeart rateBlood pressureHeart failure

Abstract

fetched live from OpenAlex

OBJECTIVE: Device supported beating heart surgery has been advocated to extend patient selection criteria for off-pump surgery. This article reports the initial experimental and clinical results with a novel paracardial right ventricular support device. METHODS: Preclinical experiments were performed in two pigs. Ten elective patients with triple vessel disease were subjected to beating heart coronary artery bypass grafting surgery during right ventricular support by the paracardial device. Measurements included intraoperative hemodynamics during cardiac tilting, perioperative left ventricular ejection fraction (LVEF), hemolysis parameters, mortality and major morbidity events. RESULTS: A mean of 3.2 +/- 0.2 distal anastomoses per patient were performed. Mean arterial pressure and central venous oxygen saturation remained stable during cardiac tilting. Perioperative LVEF did not vary significantly. Sixty-day mortality and postoperative infarction rate were 0%. Functional Canadian Cardiovascular Society class at 6 days after surgery was 1.2 +/- 0.1 vs. 3.3 +/- 0.2 pre-operatively. CONCLUSION: In this initial clinical experience, application of the novel paracardial right ventricular support device proved to be safe and efficient.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.547

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 head0.031
GPT teacher head0.301
Teacher spread0.270 · 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 designBench or experimental
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

Citations1
Published2004
Admission routes1
Has abstractyes

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