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Early Experience With Robotically Assisted Internal Thoracic Artery Harvest

2002· article· en· W2325016977 on OpenAlexaff
W. Douglas Boyd, Bob Kiaii, Kojiro Kodera, Reiza Rayman, Walid Abukhudair, Shafie Fazel, Wojciech B. Dobkowski, Sugantha Ganapathy, George Jablonsky, Richard J. Novick

Bibliographic record

VenueSurgical Laparoscopy Endoscopy & Percutaneous Techniques · 2002
Typearticle
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsHarmonic scalpelMedicineInsufflationSurgeryDissection (medical)Blood loss

Abstract

fetched live from OpenAlex

We sought to determine the efficacy of using robotic assistance to facilitate endoscopic harvesting of internal thoracic arteries (ITAs). A total of 104 patients had ITAs harvested endoscopically with use of both the AESOP 3000 system (Computer Motion, Goleta, CA, U.S.A.) and Zeus robotic telesurgical system (Computer Motion). All ITAs were harvested with a harmonic scalpel (Ethicon Endosurgery, Cincinnati, OH, U.S.A.). With the left lung collapsed, ITAs were harvested with CO2 insufflation through three 5-mm ports in the left chest. All patients tolerated insufflation without hemodynamic compromise. Average ITA harvest time was 61.3 +/- 20.9 minutes. Intraoperative graft flows averaged 36.3 +/- 22.4 mL/min. There were three distal ITA injuries; all other vessels were patent after harvesting and demonstrated no angiographic evidence of injury. This article demonstrates a technique by which ITA can be safely harvested totally endoscopically with use of computer-enhanced robotic systems and a harmonic scalpel, allowing complete pedicle dissection through 5-mm ports with minimal ITA manipulation.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.288
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.

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

Citations21
Published2002
Admission routes1
Has abstractyes

Explore more

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