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Record W2025302050 · doi:10.1177/107155170401100203

Robot-Assisted Cardiac Surgery

2004· review· en· W2025302050 on OpenAlex
R. Rayman

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSurgical Innovation · 2004
Typereview
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineBypass graftingMammary arteryCardiac surgerySurgeryRobotMedian sternotomyRevascularizationRobotic surgeryArteryCardiologyArtificial intelligenceMyocardial infarction

Abstract

fetched live from OpenAlex

The use of robotics is evolving in cardiac surgery. Robots allow minimally invasive techniques to be applied to ischemic heart and valve disease. Notably, this frees the patient from sternotomy, allowing a quick recovery while preserving the most critical aspects of the surgical procedure. The increasing use of stents for revascularization is significant. For best results to the patient, the graft of the left internal mammary artery (LIMA) to the left anterior descending artery (LAD) is a mainstay of symptom-free survival. Stenting and robotic LIMA-to-LAD grafting in a one-staged or two-staged approach may be an attractive combined specialty treatment. This would offer best practices to the patient, along with the best technologies available. In this chapter, the most common techniques in cardiac robotic surgery are outlined. Procedural steps are described, and their expanding indications for use discussed. Additionally, a focus on combining technologies for new treatments is considered.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.081
GPT teacher head0.363
Teacher spread0.281 · 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