Long-term Retention of a 3-Dimensional Educational Computer Model of the Larynx
Bibliographic record
Abstract
OBJECTIVES: To determine the long-term retention of a 3-dimentional (3-D) educational computer model of the larynx to teach laryngeal anatomy and to compare it with standard written instruction (SWI). DESIGN: Prospective randomized controlled trial. SETTING: University education program. PARTICIPANTS: One hundred health care students. INTERVENTIONS: For short-term assessment, 50 students were randomized to the 3-D model and 50 to SWI and were tested using a 20-question laryngeal test. Six months later, the same students were invited to retake the laryngeal anatomy test to examine long-term retention. MAIN OUTCOME MEASURE: The score on a 20-item Web-based test that assessed the students' level of knowledge of laryngeal anatomy approximately 6 months after their initial exposure to the laryngeal anatomy teaching intervention. RESULTS: Sixty-two students retook the test: 3-D (n = 30) and SWI (n = 32). No significant difference was noted in mean scores (P = .54) and change in scores (P = .59) between short- and long-term retention on the laryngeal anatomy test. There was a trend toward an increase in 3-D scores in both groups (P = .07) and a significant increase in 3-D scores in the 3-D group only (P = .049). CONCLUSIONS: A low-fidelity model (SWI) is just as effective as a high-fidelity model (3-D) in teaching laryngeal anatomy. The acquired knowledge from either educational intervention may last up to 6 months for long-term retention. This study is one of the few in medical education to examine long-term retention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".