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Record W2801292480 · doi:10.1002/rcs.1914

Augmented reality for the surgeon: Systematic review

2018· review· en· W2801292480 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

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2018
Typereview
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsAugmented realityHead-up displayWearable computerComputer scienceOptical head-mounted displayHead (geology)OverlayWearable technologyHuman–computer interactionMultimediaComputer visionEmbedded system

Abstract

fetched live from OpenAlex

INTRODUCTION: Since the introduction of wearable head-up displays, there has been much interest in the surgical community adapting this technology into routine surgical practice. METHODS: We used the keywords augmented reality OR wearable device OR head-up display AND surgery using PubMed, EBSCO, IEEE and SCOPUS databases. After exclusions, 74 published articles that evaluated the utility of wearable head-up displays in surgical settings were included in our review. RESULTS: Across all studies, the most common use of head-up displays was in cases of live streaming from surgical microscopes, navigation, monitoring of vital signs, and display of preoperative images. The most commonly used head-up display was Google Glass. Head-up displays enhanced surgeons' operating experience; common disadvantages include limited battery life, display size and discomfort. CONCLUSIONS: Due to ergonomic issues with dual-screen devices, augmented reality devices with the capacity to overlay images onto the surgical field will be key features of next-generation surgical head-up displays.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.681
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0040.001
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 head0.100
GPT teacher head0.380
Teacher spread0.281 · 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