MétaCan
Menu
Back to cohort
Record W2736200932 · doi:10.5489/cuaj.4262

Techniques: Utility of a 3D printed bladder model for teaching minimally invasive urethrovesical anastomosis

2017· article· en· W2736200932 on OpenAlexaffvenue
Nathan C. Wong, Jen Hoogenes, Yanbo Guo, Mackenize A. Quantz, Edward D. Matsumoto

Bibliographic record

VenueCanadian Urological Association Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAnastomosisMedicineInvasive surgeryProstatectomyUrologyLaparoscopic radical prostatectomy3d printedSurgeryBiomedical engineeringInternal medicineProstate

Abstract

fetched live from OpenAlex

The urethrovesical anastomosis (UVA) is one of the most challenging steps during a minimally invasive radical prostatectomy. Not surprisingly, minimally invasive, in particular laparoscopic, prostatectomy is associated with a steep learning curve.1 With competency-based training on the horizon, as well as the recent reduction in resident work hours, surgical educators have shifted some training outside of the operating room into surgical skills labs. To reduce learning curves and improve resident education at our centre, we use a hands-on 3D printed bladder bench model to emulate the UVA task during a minimally invasive prostatectomy.

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)0.001
metaresearch head score (Gemma)0.008
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 score0.150
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.042
GPT teacher head0.306
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations16
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Urological Association JournalSame topicSurgical Simulation and TrainingFrench-language works237,207