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Record W2882705097 · doi:10.7759/cureus.2969

Skill Acquisition, Assessment, and Simulation in Minimal Access Surgery: An Evolution of Technical Training in Surgery

2018· review· en· W2882705097 on OpenAlexaff
Ashley Vergis, Sarah N. Steigerwald

Bibliographic record

VenueCureus · 2018
Typereview
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsMedicineCompetence (human resources)Dreyfus model of skill acquisitionSimulation trainingSurgical simulationMedical educationMedical physicsSurgerySimulationComputer science

Abstract

fetched live from OpenAlex

Diminishing resources and expanding technologies, such as minimal access surgery, have complicated the acquisition and assessment of technical skills in surgical training programs. However, these challenges have been met with both innovation and an evolution in our understanding of how learners develop technical competence and how to better measure it. As these skills continue to grow in breadth and complexity, so too must the surgical education systems' ability. This literature review examines and describes the pressures placed on surgical education programs and the development of methods to ameliorate them with a focus on surgical simulation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.186
GPT teacher head0.467
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2018
Admission routes1
Has abstractyes

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