MétaCan
Menu
Back to cohort
Record W2409339440 · doi:10.7759/cureus.632

Development of a Sustainable Simulator and Simulation Program for Laparoscopic Skills Training in Haiti

2016· article· en· W2409339440 on OpenAlexafffund
Emile Damas, Chesnel Norcéide, Yvel Zephyr, Kerry-Lynn Williams, Tia Renouf, Adam Dubrowski

Bibliographic record

VenueCureus · 2016
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of NewfoundlandUniversity of Toronto
KeywordsMedicineTrainerCurriculumTraining (meteorology)Laparoscopic surgeryMedical educationLaparoscopyMedical physicsSimulationSurgeryComputer science

Abstract

fetched live from OpenAlex

Laparoscopic surgery has been shown to have many favorable effects on surgical outcomes and postoperative recovery times. However, the cost of currently available training programs, such as the Fundamentals of Laparoscopic Surgery (FLS), limits their adoption in developing countries. To address this cost constraint, educators at the Justinian University Hospital (JUH) in Northern Haiti used local materials to build their own laparoscopic skills box trainer. This trainer is used to teach all surgical and OB/GYN residents in their laparoscopic skills program. The progressive curriculum consists of seven modules, three of which are for all trainees and four of which are specifically for surgery and OB/GYN (2). The seven modules are arranged in the order of difficulty; they start with basic maneuvers and progress to complex skills. This report describes both the preparation of the seven models and evaluation of the skills that are learned. This approach may facilitate global access to feasible, progressive, and sustainable laparoscopic 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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.037
GPT teacher head0.348
Teacher spread0.312 · 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 designOther design
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

Citations9
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueCureusSame topicSurgical Simulation and TrainingFrench-language works237,207