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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".