Investigating Dental Aptitude Test (DAT) results as predictors for preclinical and clinical scores in dental school
Bibliographic record
Abstract
OBJECTIVE: The aim of the present study is to determine whether 2 current admission criteria, the perceptual ability test (PAT) and the manual dexterity test (MDT) can predict success in dental school within the Université de Montréal population. METHODS: A retrospective cohort study was conducted using the records of 854 students who graduated between 2005 and 2015. For each student, PAT and MDT scores were compared to 5 preclinical and 3 clinical classes using the Pearson correlation coefficient and regression models. T-tests were used to compare students above and below a 5-point increase in cut-off scores (PAT = 15, MDT = 10). RESULTS: The strongest relationship was found to be between PAT and preclinical scores (r = 0.329, P < 0.01). The regression analysis determined that gender, PAT and MDT predicted more of the variability of preclinical (12.7%) than of clinical scores (2.7%). Students scoring ≥10 on the MDT performed better in preclinical and clinical courses, and those scoring ≥15 on the PAT performed better in preclinical courses. However, when comparing these students to the entire group, only those scoring ≥15 on PAT differed from the group's average for preclinical scores (P = 0.029). CONCLUSION: These findings suggest the PAT and MDT have some power in predicting success in preclinical, and to a lesser extent clinical courses, and supports their use as criteria in the admissions process. There is some evidence that suggests that increasing the cut-off score may decrease the number of students with difficulties in preclinical courses.
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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 | 0.001 | 0.060 |
| 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 | 0.001 |
| 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".