Evaluating a Team-Based Learning Method for Detecting Dental Caries in Dental Students
Bibliographic record
Abstract
The purpose of the study was to investigate whether the team-based learning environment facilitated the competencyof third year dental students in caries detection and activity assessment. Corresponding data were achieved usingdigital radiographs to determine the carious lesions in three clinical cases. The distribution of the caries evaluationsfor each of the 36 students and the 12 teams were tabulated for each mesial and distal surface of each tooth for thethree patients. In the first stage of the evaluation, students worked individually for caries assessment. In the secondstage, twelve teams of three students each performed the assessment. The teams had a significantly larger percentageof accurate anterior caries evaluations (54%) than the individual students (33%) (X2 = 5.50; p = .019), but also sawsignificantly more carious lesions when there was no lesion (126%) compared to the students working singly (85%)(X2 = 7.55; p = .006). The individual students, however, were significantly more likely to incorrectly call incipientcaries active (81%) than the teams (14%).
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".