Computer‐assisted teaching of epistaxis management
Bibliographic record
Abstract
OBJECTIVES: To determine whether computer-assisted learning (CAL) is an effective tool for the instruction of technical skills. STUDY DESIGN: Prospective blinded randomized-control trial conducted on a cohort of 47 first-year medical students. METHODS: Students were instructed on two techniques of nasal packing (formal nasal pack and nasal tampon) for the management of epistaxis, using either a standard text-based article or a novel computer-based learning module. Students were evaluated on proper nasal packing technique using standardized subjective and objective outcome measures by three board-certified otolaryngologists. Blind assessments took place prior to and following instruction, using the assigned learning modality. RESULTS: There were 47 participants enrolled in the study. Both groups demonstrated improvement in performance of both packing procedures following training. A significant post-training difference favoring CAL learners over text-based learners was observed, using the global assessment of skill for both packing techniques (P < .001). Additionally, a significant post-training difference favoring CAL learners over text-based learners was observed for all checklist items for the tampon pack and five of eight items on the formal pack checklist. The vast majority of students (94.6%) indicated that if given the choice, they would prefer to learn using CAL rather than by using text-based learning materials. CONCLUSIONS: CAL learners demonstrated significantly greater improvement across both subjective and objective outcome measures when compared to the text-based group. Additionally, students favored learning via the CAL modality, which further suggests that CAL is a valuable means of imparting procedural knowledge to novice medical trainees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".