Development of a Secondary Dental-Specific Database for Active Learning of Genetics in Dentistry Programs
Bibliographic record
Abstract
OBJECTIVES: Dental students study the genetics of tooth and facial development through didactic lectures only. Meanwhile, scientists' knowledge of genetics is rapidly expanding, over and above what is commonly found in textbooks. Therefore, students studying dentistry are often unfamiliar with the burgeoning field of genetic data and biological databases. There is also a growing interest in applying active learning strategies to teach genetics in higher education. We developed a secondary database called "Genetics for Dentistry" to use as an active learning tool for teaching genetics in dentistry programs. The database archives genomic and proteomic data related to enamel and dentin formation. METHODS: We took a systematic approach to identify, collect, and organize genomic and proteomic tooth development data from primary databases and literature searches. The data were checked for accuracy and exported to Ragic to create an interactive secondary database. RESULTS: "Genetics for Dentistry," which is in its initial phase, contains information on all the human genes involved in enamel and dentin formation. Users can search the database by gene name, protein sequence, chromosomal location, and other keywords related to protein and gene function. CONCLUSIONS: "Genetics for Dentistry" will be introduced as an active learning tool for teaching genetics at the School of Dentistry of the University of Alberta. Activities using the database will supplement lectures on genetics in the dentistry program. We hope that incorporating this database as an active learning tool will reduce students' cognitive load in learning genetics and stimulate interest in new branches of science, including bioinformatics and precision dentistry.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.023 |
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".