Prospective evaluation of a web-based three-dimensional cranial nerve simulation.
Bibliographic record
Abstract
OBJECTIVE: Advancements in technology and personal computing afford the development of novel teaching modalities such as online Web-based modules. These modules are currently being incorporated into undergraduate medical curricula and, in some paradigms, have been shown to be superior to traditional methods of instruction. The purpose of this study was to evaluate the ability of a computer-assisted learning (CAL) module to demonstrate content and spatial information in the context of cranial nerve anatomy. STUDY POPULATION: Undergraduate anatomy students. METHODS: A prospective, randomized, controlled trial was conducted comparing a CAL module to traditional text-/image-based learning supplements. Indications of the participants' ability to translate spatial relationships between the trigeminal nerve and the craniofacial skeleton were assessed via a postintervention knowledge quiz. RESULTS: No significant difference was identified between the CAL module and the control group. Students in both groups performed poorly in questions testing spatial relationships. CONCLUSIONS: The CAL module used in the present study did not objectively contribute to the understanding of spatial anatomic relationships of the cranial nerves in novice students. Despite this, these modules may help pique student interest and motivation and, as such, may be used in the context of supplemental learning resources in existing university curricula.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".