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Record W2138374899 · doi:10.1089/end.2010.0741

A System for Laparoscopic Surgery Ergonomics and Skills Evaluation

2011· article· en· W2138374899 on OpenAlexafffund
Richard Fanson, Faezeh Heydari Khabbaz, Anil Kapoor, Alexandru Patriciu

Bibliographic record

VenueJournal of Endourology · 2011
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaImperial College London
KeywordsMedicineTask (project management)ShouldersTracking (education)Invasive surgeryArtificial intelligenceComputer visionSurgeryMedical physicsComputer science

Abstract

fetched live from OpenAlex

This article presents a system for tracking, recording, and analysis of instrument and surgeon's arm motion in minimally invasive surgeries. The captured trajectories can be objectively analyzed for both ergonomic assessment and skills evaluation. The system consists of two special infrared (IR) markers that are used for 6 degrees of freedom (DOF) laparoscopic instrument tracking and a set of 3DOF IR markers attached to elbows and shoulders. A compact IR camera tracks and records the markers during a standardized training task (eg, suturing). The instrument markers were purposely designed to provide good tracking while minimizing their volume. The accuracy of the instrument markers was evaluated showing a root mean square error of 0.61 mm, 1.0 mm, and 2.4 mm at distances from the camera of 0.5 m, 0.68 m, and 1 m respectively. Furthermore, some sample trajectories were recorded during an in-trainer suturing task. The Results section presents the values of basic skills metrics computed from the acquired data.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.144

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.083
GPT teacher head0.324
Teacher spread0.240 · 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 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

Citations3
Published2011
Admission routes2
Has abstractyes

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