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Record W2781038306 · doi:10.1002/rcs.1885

Camera‐augmented mobile C‐arm (CamC): A feasibility study of augmented reality imaging in the operating room

2017· article· en· W2781038306 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 · 2017
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsWorkflowFluoroscopyProtocol (science)Radiation exposureComputer scienceAugmented realitySimulationMedical physicsMedicineNuclear medicineSurgeryArtificial intelligenceDatabase

Abstract

fetched live from OpenAlex

BACKGROUND: In orthopaedic trauma surgery, image-guided procedures are mostly based on fluoroscopy. The reduction of radiation exposure is an important goal. The purpose of this work was to investigate the impact of a camera-augmented mobile C-arm (CamC) on radiation exposure and the surgical workflow during a first clinical trial. METHODS: Applying a workflow-oriented approach, 10 general workflow steps were defined to compare the CamC to traditional C-arms. The surgeries included were arbitrarily identified and assigned to the study. The evaluation criteria were radiation exposure and operation time for each workflow step and the entire surgery. The evaluation protocol was designed and conducted in a single-centre study. RESULTS: The radiation exposure was remarkably reduced by 18 X-ray shots 46% using the CamC while keeping similar surgery times. CONCLUSIONS: The intuitiveness of the system, its easy integration into the surgical workflow, and its great potential to reduce radiation have been demonstrated.

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)0.003
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 score0.159
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.081
GPT teacher head0.389
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