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Analysis of neonatal resuscitation using eye tracking: a pilot study

2017· article· en· W2750552527 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

VenueArchives of Disease in Childhood Fetal & Neonatal · 2017
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsRoyal Alexandra HospitalUniversity of Alberta
FundersUniversity of AlbertaHeart and Stroke Foundation of Canada
KeywordsNeonatal resuscitationResuscitationEye trackingMedicineMedical emergencyEmergency medicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Visual attention (VA) is important for situation awareness and decision-making. Eye tracking can be used to analyse the VA of healthcare providers. No study has examined eye tracking during neonatal resuscitation. OBJECTIVE: To test the use of eye tracking to examine VA during neonatal resuscitation. METHODS: Six video recordings were obtained using eye tracking glasses worn by resuscitators during the first 5 min of neonatal resuscitation. Videos were analysed to obtain (i) areas of interest (AOIs), (ii) time spent on each AOI and (iii) frequency of saccades between AOIs. RESULTS: Five videos were of acceptable quality and analysed. Only 35% of VA was directed at the infant, with 33% at patient monitors and gauges. There were frequent saccades (0.45/s) and most involved patient monitors. CONCLUSION: During neonatal resuscitation, VA is often directed away from the infant towards patient monitors. Eye tracking can be used to analyse human performance during neonatal resuscitation.

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)0.000
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 score0.047
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.045
GPT teacher head0.359
Teacher spread0.314 · 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