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Record W2120374623 · doi:10.1002/mrm.22664

Functional MRI‐compatible laparoscopic surgery training simulator

2010· article· en· W2120374623 on OpenAlexaff
Parisa Bahrami, Tom A. Schweizer, Fred Tam, Teodor Grantcharov, Michael D. Cusimano, Simon J. Graham

Bibliographic record

VenueMagnetic Resonance in Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of TorontoBaycrest HospitalSt. Michael's Hospital
Fundersnot available
KeywordsLaparoscopyComputer scienceEye–hand coordinationTask (project management)Laparoscopic surgeryPhysical medicine and rehabilitationSimulationMedicineArtificial intelligenceRadiology

Abstract

fetched live from OpenAlex

During the past few years, laparoscopy has become the gold standard for some surgical procedures and its applications continue to expand. Because of multiple factors such as loss of tactile perception, two-dimensional visualization of the three-dimensional surgical field, and demanding bimanual hand-eye coordination, special training is required to achieve proficiency with laparoscopy. In this study, as the first step toward evidence-based development of strategies to improve the quality of laparoscopy training from brain activity and behavior relationships, a laparoscopy training simulator was developed for use in functional MRI. Experiments confirmed the functional MRI compatibility of the device. Representative behavioral and functional MRI results for two subjects showed the feasibility of using this simulator to investigate the brain activation associated with components of laparoscopic task performance. To our knowledge, this is the first study that directly looks at the functional MRI brain activation during complex surgical training tasks.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.058
GPT teacher head0.309
Teacher spread0.251 · 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 designObservational
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
Published2010
Admission routes1
Has abstractyes

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