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Record W2114706405 · doi:10.3109/10929088.2012.727641

Sensor fusion for laparoscopic surgery skill acquisition

2012· article· en· W2114706405 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueComputer Aided Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsDreyfus model of skill acquisitionComputer scienceLaparoscopic surgeryOrientation (vector space)Medical physicsSimulationPhysical medicine and rehabilitationHuman–computer interactionArtificial intelligenceLaparoscopyMedicineSurgery

Abstract

fetched live from OpenAlex

Surgical techniques are becoming more complex and require substantial training to master. The development of automated, objective methods to analyze and evaluate surgical skill is necessary to provide trainees with reliable and accurate feedback during their training programs. We present a system to capture, visualize, and analyze the movements of a laparoscopic surgeon for the purposes of skill evaluation. The system records the upper body movement of the surgeon, the position, and orientation of the instruments, and the force and torque applied to the instruments. An empirical study was conducted using the system to record the performances of a number of surgeons with a wide range of skill. The study validated the usefulness of the system, and demonstrated the accuracy of the measurements.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.304
Teacher spread0.248 · 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