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Record W1999784657 · doi:10.1155/2014/975752

Combining First-Person Video and Gaze-Tracking in Medical Simulation: A Technical Feasibility Study

2014· article· en· W1999784657 on OpenAlexaff
Adam Szulewski, Daniel Howes

Bibliographic record

VenueThe Scientific World JOURNAL · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsGazeTracking (education)Computer scienceEye trackingQuality (philosophy)Applied psychologyProcess (computing)PsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Crisis decision-making is an important responsibility of the resuscitation team leader but a difficult process to study. The purpose of this pilot study was to explore the potential of gaze-tracking technology to study decision-making and leadership behaviours in simulated medical emergencies. We studied five physicians with a broad range of experience in a simulated medical emergency using gaze-tracking glasses. Subjects were interviewed immediately after the scenario while viewing a first-person recording of their performance with a superimposed gaze indicator. The recordings were then studied independently by two reviewers, and rated for quality and their observations collated. Portable gaze-tracking devices were found to be useful and effective tools for studying information gathering and decision-making behaviours in simulated medical emergencies. The data obtained in this study provided information about the discrepancy between what each participant looked at compared to what each participant consciously noted. Analysis of the data also identified a number of recurrent gaze patterns performed by team leaders that could be used as end-points in future research. Gaze-tracking in resuscitation medicine is a new and promising field of study. The potential to study crisis decision-making behaviours, and cognitive load, as well as differences between novice and expert team leaders is substantial.

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)0.008
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.347
Teacher spread0.309 · 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

Citations26
Published2014
Admission routes1
Has abstractyes

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