MétaCan
Menu
Back to cohort
Record W2998907477 · doi:10.2106/jbjs.19.00629

Fully Immersive Virtual Reality for Total Hip Arthroplasty

2020· article· en· W2998907477 on OpenAlex
Kartik Logishetty, Wade Gofton, Branavan Rudran, Paul E. Beaulé, Justin Cobb

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

VenueJournal of Bone and Joint Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsVirtual realityCurriculumMedicineOsteotomyLearning curvePhysical therapyDuration (music)Physical medicine and rehabilitationSurgeryPsychologyComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: Fully immersive virtual reality (VR) uses headsets to situate a surgeon in a virtual operating room to perform open surgical procedures. The aims of this study were to determine (1) if a VR curriculum for training residents to perform anterior approach total hip replacement (AA-THR) was feasible, (2) if VR enabled residents' performance to be measured objectively, and (3) if cognitive and motor skills that were learned with use of VR were transferred to the physical world. METHODS: The performance of 32 orthopaedic residents (surgical postgraduate years [PGY]-1 through 4) with no prior experience with AA-THR was measured during 5 consecutive VR training and assessment sessions. Outcome measures were related to procedural sequence, efficiency of movement, duration of surgery, and visuospatial precision in acetabular component positioning and femoral neck osteotomy, and were compared with the performance of 4 expert hip surgeons to establish competency-based criteria. Pretraining and post-training assessments on dry bone models were used to assess the transfer of visuospatial skills from VR to the physical world. RESULTS: Residents progressively developed surgical skills in VR on a learning curve through repeated practice, plateauing, on average, after 4 sessions (4.1 ± 0.6 hours); they reached expert VR levels for 9 of 10 metrics (except femoral osteotomy angle). Procedural errors were reduced by 79%, assistive prompts were reduced by 70%, and procedural duration was reduced by 28%. Dominant and nondominant hand movements were reduced by 35% and 36%, respectively, and head movement was reduced by 44%. Femoral osteotomy was performed more accurately, and acetabular implant orientation improved in VR assessments. In the physical world assessments, experts were more accurate than residents prior to simulation, but were matched by residents after simulation for all of the metrics except femoral osteotomy angle. The residents who performed best in VR were the most accurate in the physical world, while 2 residents were unable to achieve competence despite sustained practice. CONCLUSIONS: For novice surgeons learning AA-THR skills, fully immersive VR technology can objectively measure progress in the acquisition of surgical skills as measured by procedural sequence, efficiency of movement, and visuospatial accuracy. Skills learned in this environment are transferred to the physical environment.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.078
GPT teacher head0.289
Teacher spread0.211 · 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