MétaCan
Menu
Back to cohort
Record W2616771772 · doi:10.7759/cureus.1253

A Practice Platform for Systematic Development of Microsurgical Instrument Technique

2017· article· en· W2616771772 on OpenAlexaff
Darius Bägli, Rakan I. Odeh, Frank J. Penna, Ethan D. Grober, Nicolás Fernández, Armando J. Lorenzo, Lisa Satterthwaite, Adam Dubrowski

Bibliographic record

VenueCureus · 2017
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of TorontoMemorial University of NewfoundlandOccupational Cancer Research CentreHospital for Sick Children
Fundersnot available
KeywordsMedicineMicrosurgeryMedical physicsMagnificationSet (abstract data type)FidelityMedical educationSurgeryComputer scienceArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

Many surgical disciplines, particularly those specializing in the pediatric age group, use microsurgical instruments with the assistance of either optical loupe or microscope magnification to perform high precision surgical procedures. We developed a course consisting of two parts: Part 1 consists of low fidelity, inanimate exercises or training and practice platform, and part 2 employed a rat model. In this report, we describe and provide templates for the first part of the course, namely the practice platform as an integral set of six low-fidelity exercises, each focusing on a specific aspect of instrument handling required to master the later use of the instruments during actual microsurgery. This platform is made to systematically and efficiently improve the microsurgical skills of junior as well as advanced surgical trainees.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.084
GPT teacher head0.378
Teacher spread0.294 · 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 designNot applicable
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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCureusSame topicSurgical Simulation and TrainingFrench-language works237,207