MétaCan
Menu
Back to cohort
Record W2596359566 · doi:10.1117/12.2256483

Preliminary development of augmented reality systems for spinal surgery

2017· article· en· W2596359566 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

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAugmented realityComputer scienceVisualizationComputer visionImaging phantomOverlayArtificial intelligenceMixed realityNavigation systemHuman–computer interactionRadiologyMedicine

Abstract

fetched live from OpenAlex

Surgical navigation has been more actively deployed in open spinal surgeries due to the need for improved precision during procedures. This is increasingly difficult in minimally invasive surgeries due to the lack of visual cues caused by smaller exposure sites, and increases a surgeon’s dependence on their knowledge of anatomical landmarks as well as the CT or MRI images. The use of augmented reality (AR) systems and registration technologies in spinal surgeries could allow for improvements to techniques by overlaying a 3D reconstruction of patient anatomy in the surgeon’s field of view, creating a mixed reality visualization. The AR system will be capable of projecting the 3D reconstruction onto a field and preliminary object tracking on a phantom. Dimensional accuracy of the mixed media will also be quantified to account for distortions in tracking.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.034
GPT teacher head0.274
Teacher spread0.240 · 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