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Record W2793346856 · doi:10.1002/rcs.1891

Robotic‐assisted coronary artery bypass surgery: an 18‐year single‐centre experience

2018· article· en· W2793346856 on OpenAlex

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

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsUniversité de Saint-BonifaceSt. Boniface HospitalLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineArteryAnastomosisBypass graftingSurgeryRevascularizationCardiac catheterizationCardiologyMyocardial infarction

Abstract

fetched live from OpenAlex

BACKGROUND: Minimally invasive robot-assisted direct coronary artery bypass (RADCAB) has emerged as a feasible minimally invasive surgical technique for revascularization that might offer several potential advantages over conventional approaches. We present our 18-year experience in RADCAB. METHODS: Between February 1998 and February 2016, 605 patients underwent RADCAB. Patients underwent post-procedural selective graft patency assessment using cardiac catheterization. RESULTS: The mortality rate was 0.3%. The rate of conversion to sternotomy for any cause was reduced from 16.0% of the first 200 cases to 6.9% of the last 405 patients. The patency rate of the LITA-to-LAD anastomosis was 97.4%. Surgical re-exploration for bleeding occurred in 1.8% of patients, and the transfusion rate was 9.2%. Average ICU stay was 1.2 ± 1.4 days, and average hospital stay was 4.8 ± 2.9 days. CONCLUSIONS: Robot-assisted coronary artery bypass grafting is safe, feasible and it seems to represent an effective alternative to traditional coronary artery bypass grafting in selected patients.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0010.000

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 head0.042
GPT teacher head0.305
Teacher spread0.263 · 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