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Record W2893392684 · doi:10.1097/sap.0000000000001594

Making Augmented and Virtual Reality Work for the Plastic Surgeon

2018· review· en· W2893392684 on OpenAlexaff
Jonathan Kanevsky, Tyler Safran, Dino Zammit, Samuel J. Lin, Mirko S. Gilardino

Bibliographic record

VenueAnnals of Plastic Surgery · 2018
Typereview
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineAugmented realityVirtual realityThe InternetVisualizationResource (disambiguation)Point (geometry)Health careMultimediaHuman–computer interactionWorld Wide WebComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Augmented and virtual reality is an evolving technology at the forefront of medicine. It can provide physicians with hands-free, real-time access to the vast resources of the Internet and electronic medical records, allowing simultaneously recording of clinical encounters or procedures. Mixed reality platforms can be applied as a clinical tool, educational resource, or as an aid in enhancing communication in health care. This article will explore how various augmented and virtual reality platforms have enabled real-time visualization of patient information, recording of surgical cases, point-of-view photography, and intraoperative consults-all while remaining sterile in the operating room. Although this technology is of potential value to a number of different surgical and medical specialties, plastic surgery is ideally suited to lead this charge.

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)0.002
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.012
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 science0.0000.000
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 head0.389
GPT teacher head0.441
Teacher spread0.052 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations23
Published2018
Admission routes1
Has abstractyes

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