Evaluation of Applicants to Predoctoral Dental Education Programs: Review of the Literature
Bibliographic record
Abstract
This review finds that college GPA and DAT scores provide dental schools in the United States and Canada with defensible methods for selecting students. College GPA seems the best predictor of academic performance in dental school. The academic average (AA) of the DAT is a better predictor than is the perceptual ability test (PAT), but dental educators who believe that evidence of manual dexterity or perceptual ability must be a part of the admissions decision can find enough supporting evidence to justify doing so. When added to college GPA and the AA, information from the PAT may in fact enhance predictability. There is also evidence, however, that manual skills can be learned during routine dental curricular experiences. Overall, conventional admissions criteria at best account for about 40 percent of the variance in dental school performance, and most of this variance occurs during the early years of the curriculum. Studies are lacking for evaluating criteria that may predict success in admitting students for preferentially addressing current challenges, including achieving diversity of the workforce, ensuring access to care for all, interprofessional health care, ethics and professionalism, filling faculty positions, and conducting needed research. Schools should periodically validate all of their admissions criteria against expected performances and make corresponding adjustments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".