Evolution in the Ability to Perform Preclinical Preparations by Undergraduate Dentistry Students
Bibliographic record
Abstract
This study evaluated the effectiveness of traditional teaching methods in developing students’ practical skills in the Laboratory Practice in Indirect Restorations (LPIR) course at the School of Dentistry, Universidade Federal de Minas Gerais (FAO-UFMG). A qualitative before-and-after study was conducted with 25 fifth-semester undergraduate dental students to assess the quality of cavity preparations for indirect esthetic onlay restorations performed on mannequin teeth. Preparations were evaluated twice during the academic semester. The first evaluation occurred after a professor-led demonstration, and the second followed a supervised practical repetition period. A digital assessment tool was used to compare preparation characteristics and monitor student progress. The results demonstrated a significant reduction in preparation errors over time, indicating that the traditional teaching methods applied effectively enhanced the students’ clinical performance. Furthermore, the digital tool was found to be a useful supplement to instructor evaluations, identifying frequent errors and enabling more precise, personalized feedback. These findings indicate that integrating digital assessment tools and traditional teaching strategies is a promising approach for improving dental education and student learning outcomes.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".