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Record W2012771266 · doi:10.1016/j.otohns.2008.11.033

Interactive Internet‐based cases for undergraduate otolaryngology education

2009· article· en· W2012771266 on OpenAlexaffabout
Thileeban Kandasamy, Kevin Fung

Bibliographic record

VenueOtolaryngology · 2009
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsWestern University
FundersDivision of Undergraduate Education
KeywordsRandomized controlled trialMedicineOtorhinolaryngologyGroup BTest (biology)CurriculumMedical educationPhysical therapyPsychologyInternal medicineSurgeryPedagogy

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine the feasibility and effectiveness of virtual-patient computer-assisted instruction (CAI) in pre-clerkship undergraduate otolaryngology education. STUDY DESIGN: Prospective, randomized, controlled trial. SUBJECTS AND METHODS: Second-year medical students at the University of Western Ontario, Canada, were randomized into two groups: group A was given a CAI module and group B was presented with two Internet review articles, both covered specific learning objectives for pediatric stridor. Students completed randomized pre- and post-tests and a questionnaire one week later. RESULTS: Fifty-five students completed the study with 28 in group A and 27 in group B. Mean pretest scores were 59.1% in group A and 59.8% in group B (95% CI = -7.9% to 10.4%). Mean post-test scores were significantly elevated in group A (84.6%, P < 0.001) and group B (74.3%, P = 0.008). Group A had a significantly higher (P = 0.02) mean post-test score than group B (mean difference of 10.2%). Students spent significantly more time (P < 0.001) reading text articles (25.5 minutes) than completing the CAI (9.06 minutes). Forty-one (66%) students completed the survey. Thirty-six (88%) respondents indicated that they preferred CAI to online articles. CONCLUSION: CAI is a feasible, effective, and efficient means of enhancing self-directed learning as supplementation to the pre-clerkship undergraduate otolaryngology curriculum.

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)0.002
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 score0.443
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.017
GPT teacher head0.343
Teacher spread0.326 · 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

Citations39
Published2009
Admission routes2
Has abstractyes

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