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Record W4367173830 · doi:10.3138/jvme-2022-0121

Comparison of Traditional Didactic Versus Additional Hands-On Simulation Training in the Performance of Basic Life Support in Veterinary Students—A Prospective, Blinded, Randomized Study

2023· article· en· W4367173830 on OpenAlexvenueno aff
Rebecca Walton, Julie Riha, Tamara M. Swor, Jamie J. Kopper, Lingnan Yuan, Jonathan P. Mochel, Meredith t Hoen, April E. Blong

Bibliographic record

VenueJournal of Veterinary Medical Education · 2023
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsBasic life supportCardiopulmonary resuscitationSimulation trainingMedical educationTraining (meteorology)MedicinePsychologyComputer scienceResuscitationSimulationAnesthesia

Abstract

fetched live from OpenAlex

Cardiopulmonary resuscitation (CPR) is a critical skill for veterinarians, but the most effective training methods and techniques are still unknown. In human medicine, simulation training enhances both knowledge and performance of basic life support CPR. This study evaluated the comparative effectiveness of didactic versus a combination of didactic and simulation training on performance and understanding of basic life support techniques in second-year veterinary medical students.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.306
GPT teacher head0.512
Teacher spread0.206 · 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 designRandomized trial
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

Citations2
Published2023
Admission routes1
Has abstractyes

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