Embryology and Histology Education in North American Dental Schools: The Basic Science Survey Series
Bibliographic record
Abstract
As part of the Basic Science Survey Series (BSSS) for Dentistry, members of the American Dental Education Association (ADEA) Anatomical Sciences Section surveyed faculty members teaching embryology and histology courses at North American dental schools. The survey was designed to assess, among other things, curriculum content, utilization of laboratories, use of computer-assisted instruction (CAI), and recent curricular changes. Responses were received from fifty-nine (88.1 percent) of the sixty-seven U.S. and Canadian dental schools. Findings suggest the following: 1) a trend toward combining courses is evident, though the integration was predominantly discipline-based; 2) embryology is rarely taught as a stand-alone course, as content is often covered in gross anatomy, oral histology, and/or in an integrated curriculum; 3) the number of contact hours in histology is decreasing; 4) a trend toward reduction in formal laboratory sessions, particularly in embryology, is ongoing; and 5) use of CAI tools, including virtual microscopy, in both embryology and histology has increased. Additionally, embryology and histology content topic emphasis is identified within this study. Data, derived from this study, may be useful to new instructors, curriculum and test construction committees, and colleagues in the anatomical sciences, especially when determining a foundational knowledge base.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".