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Record W2616237428 · doi:10.1002/rcs.1828

Mastery Learning – does the method of learning make a difference in skills acquisition for robotic surgery?

2017· article· en· W2616237428 on OpenAlex
Karen L. Siroen, Christopher D. Ward, Abelardo Escoto, Michael D. Naish, Y Bureau, Rajni V. Patel, Christopher M. Schlachta, Sayra Cristancho, Ana Luisa Trejos

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 institutionsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsRobotic surgeryRandom walkThread (computing)Significant differenceTrainerMedicineSurgeryComputer sciencePhysical therapyMathematicsStatisticsInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Few studies compare the effectiveness of blocked vs random practice conditions in minimally invasive surgery training, and none have evaluated these in robotic surgery training. METHODS: Surgical System (dVSS) were used to compare practice conditions. Forty-two participants were randomized into blocked and random practice groups. Each participant performed five tasks: Ring Walk, Thread the Rings, Needle Targeting, Suture Sponge and Tubes Level 2. Transfer to the dVSS was also assessed. RESULTS: No significant differences were observed between the two groups, except for a few instances. For example, during Ring Walk, the random group performed significantly faster than the blocked group (100.78 ± 5.26 s vs 121.59 ± 5.26 s, p < 0.01). CONCLUSIONS: The study results do not follow the current evidence presented in the education literature. This is the first time that blocked versus random practice was tested for robotic surgery training.

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)0.002
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: none
Teacher disagreement score0.644
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 integrity0.0000.001
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 head0.043
GPT teacher head0.347
Teacher spread0.304 · 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