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Record W1981651620 · doi:10.1109/biorob.2012.6290763

Development of a hands-free pointer for instruction during minimally invasive surgery

2012· article· en· W1981651620 on OpenAlexaff
Christopher D. Ward, Ana Luisa Trejos, Michael D. Naish, Rajni V. Patel, Karen L. Siroen, Christopher M. Schlachta

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsDomtar (Canada)Lawson Health Research Institute
FundersStryker
KeywordsHeadsetHands freeInvasive surgeryPointer (user interface)Computer scienceSimulationInertial frame of referenceSurgeryHuman–computer interactionArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

The WHaSP, a Wireless Hands-free Surgical Pointer system has been developed to address the challenges of providing instruction during minimally invasive surgery. A headset worn by the instructing surgeon, and tracked using infrared and inertial technologies, is used to control a pointer that is overlaid on the surgical video. The combination of inertial and infrared tracking provides optimal control of the pointer and high accuracy. Experiments have been performed to validate the performance of the WHaSP prototype as compared to a commercially available hands-free pointing system. The results demonstrate improved performance, independent from the distance to the monitor. A new prototype is currently under construction that greatly improves the stability, ergonomics, and ease of use of the system. The WHaSP has the potential to significantly improve surgical instruction during minimally invasive surgery.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.056
GPT teacher head0.285
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations0
Published2012
Admission routes1
Has abstractyes

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