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Objective assessment of surgeon’s psychomotor skill using virtual reality module

2019· article· en· W2938963653 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.

Bibliographic record

VenueIndonesian Journal of Electrical Engineering and Computer Science · 2019
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsKensington Health
Fundersnot available
KeywordsPsychomotor learningVirtual realitySimulationComputer sciencePoint (geometry)Path (computing)Motion (physics)Artificial intelligenceHuman–computer interactionComputer visionPsychologyMathematics

Abstract

fetched live from OpenAlex

<span>This study aims to identify measurable parameters that could be used as objective assessment parameters to evaluate surgical dexterity using computer-based assessment module. A virtual reality module was developed to measure dynamic and static hand movements in a bimanual experimental setting. The experiment was conducted with sixteen subjects divided into two groups: surgeons (N = 5) and non-surgeons (N = 11). Results showed that surgeons outperformed the non-surgeons in motion path accuracy, motion path precision, economy of movement, motion smoothness, end-point accuracy and end-point precision. The six objective parameters can complement existing assessment methods to better quantify a trainee’s performance. These parameters also could provide information of hand movements that cannot be measured with the human eye. An assessment strategy using appropriate parameters could help trainees learn on computer-based systems, identify their mistakes and improve their skill towards the competency, without relying too much on bench models and cadavers.</span>

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.290
Teacher spread0.276 · 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