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Record W2071082414 · doi:10.1177/155335060501200405

Efficacy of Novel Robotic Camera vs a Standard Laparoscopic Camera

2005· article· en· W2071082414 on OpenAlexaboutno aff
Vivian E. Strong, Nancy J. Hogle, Dennis Fowler

Bibliographic record

VenueSurgical Innovation · 2005
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLaparoscopic surgeryRobotic surgerySurgeryArtificial intelligenceComputer visionLaparoscopyGeneral surgeryComputer science

Abstract

fetched live from OpenAlex

To improve visualization during minimal access surgery, a novel robotic camera has been developed. The prototype camera is totally insertable, has 5 degrees of freedom, and is remotely controlled. This study compared the performance of laparoscopic surgeons using both a laparoscope and the robotic camera. The MISTELS (McGill Inanimate System for the Training and Evaluation of Laparoscopic Skill) tasks were used to test six laparoscopic fellows and attending surgeons. Half the surgeons used the laparoscope first and half used the robotic camera first. Total scores from the MISTELS sessions in which the laparoscope was used were compared with the sessions in which the robotic camera was used and then analyzed with a paired t test (P < .05 was considered significant). All six surgeons tested showed no significant difference in their MISTELS task performance on the robotic camera compared with the standard laparoscopic camera. The mean MISTELS score of 963 for all subjects who used a laparoscope and camera was not significantly different than the mean score of 904 for the robotic camera (P = .17). This new robotic camera prototype allows for equivalent performance on a validated laparoscopic assessment tool when compared with performance using a standard laparoscope.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.048
GPT teacher head0.343
Teacher spread0.294 · 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 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

Citations10
Published2005
Admission routes1
Has abstractyes

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