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Record W2169164695 · doi:10.1532/hsf.1160

Robotic Surgery, the First 100 Cases: Where Do We Go from Here?

2005· article· en· W2169164695 on OpenAlexaff
Alan H. Menkis, Kojiro Kodera, Bob Kiaii, Stuart A. Swinamer, Reiza Rayman, W. Douglas Boyd

Bibliographic record

VenueThe Heart Surgery Forum · 2005
Typearticle
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineZEUS (particle detector)SurgeryMitral valveArteryCardiac surgery

Abstract

fetched live from OpenAlex

Abstract Background: Since the robot-assisted cardiac surgery program at this center was initiated in September 1998 the results have been regularly critically evaluated. We report a retrospective review of the first 100 robotic procedures and their evolution. Methods: Between September 1998 and May 2001, 146 patients underwent robot-assisted procedures. All procedures were performed using the Aesop robotically controlled camera or the Zeus robotic system. A harmonic scalpel was used for all internal thoracic artery (ITA) dissections whether the dissections were performed manually or with the Zeus robotic system. Results: There were 123 closed-heart and 23 open-heart procedures, which included 8 atrial-septal defect repairs, 11 mitral valve repairs, 4 mitral valve replacements, 57 Aesop ITA takedowns, 68 Zeus ITA takedowns, and 13 totally endoscopic coronary artery bypass grafts. Graft patency in Aesop and Zeus ITA takedown groups was 96%. All the patients were New York Heart Association class I after their procedures. Conclusion: With the development of surgical robots, it has been possible to perform endoscopic cardiac surgery for selected cases. Future directions will be demonstrated, including telementoring, telesurgery, and Zeus-assisted initiatives in cardiac surgery and other surgical disciplines.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
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
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.262
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2005
Admission routes1
Has abstractyes

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