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A Preliminary Study in Using Virtual Reality to Train Dental Students

2004· article· en· W1856827744 on OpenAlexaff
Vicki R. LeBlanc, Alice Urbankova, F Hadavi, Richard M. Lichtenthal

Bibliographic record

VenueJournal of Dental Education · 2004
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsOccupational Cancer Research CentreUniversity of Toronto
Fundersnot available
KeywordsVirtual realityCurriculumMedical educationDental educationComputer scienceSimulationPsychologyMedicineHuman–computer interactionPedagogy

Abstract

fetched live from OpenAlex

This study compared virtual reality simulator-enhanced training with laboratory-only practice on the development of dental technical skills. Sixty-eight students were randomly assigned to practice their skills in either a traditional preclinical dentistry laboratory or in combination with a virtual reality simulator. The results indicate that students who trained with the virtual reality simulator between six and ten hours improved significantly more than did the students in the control group from the first examination of the year to the final examination of the year. These results indicate that the use of virtual reality simulators holds promise for the training of future dentists. Additional research is necessary to determine the ideal implementation of virtual reality simulators into traditional dentistry curricula.

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)0.007
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.059
GPT teacher head0.432
Teacher spread0.373 · 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 designObservational
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

Citations138
Published2004
Admission routes1
Has abstractyes

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