MétaCan
Menu
Back to cohort
Record W3214444446 · doi:10.1016/j.eururo.2021.10.030

Effect of Simulation-based Training on Surgical Proficiency and Patient Outcomes: A Randomised Controlled Clinical and Educational Trial

2021· article· en· W3214444446 on OpenAlex
Abdüllatif Aydın, Kamran Ahmed, Takashige Abe, Nicholas Raison, Mieke Van Hemelrijck, Hans Garmo, Hashim U. Ahmed, Furhan Mukhtar, Ahmed Al‐Jabir, Oliver Brunckhorst, Nobuo Shinohara, Wei Zhu, Guohua Zeng, John P. Sfakianos, Mantu Gupta, Ashutosh Tewari, Ali Serdar Gözen, Jens Rassweiler, Andreas Skolarikos, Thomas Kunit, Thomas Knoll, Felix Moltzahn, George N. Thalmann, Andrea G. Lantz Powers, Ben H. Chew, Kemal Sarıca, Muhammad Shamim Khan, Prokar Dasgupta

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

VenueEuropean Urology · 2021
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of British ColumbiaDalhousie University
FundersNational Institute for Health and Care Research
KeywordsMedicineConfidence intervalRandomized controlled trialSimulation trainingHazard ratioTrial registrationPhysical therapySurgeryInternal medicineSimulation

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
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 head0.044
GPT teacher head0.371
Teacher spread0.326 · 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