Interactive E‐Learning Module: Enhancing Panoramic Radiograph Interpretation Skills of Dental Students
Bibliographic record
Abstract
OBJECTIVES: Two interactive e-learning modules were developed, focusing on dental panoramic radiograph (DPR) interpretation and a virtual dental clinic (VDC) for communication skills. The aim of the study was twofold: to quantitatively evaluate the effectiveness of the modules in enhancing students' skills in interpreting DPRs and complying with reporting standards in clinical practice, and to qualitatively assess students' perceptions of the module's effectiveness. METHODS: A mixed-methods cohort study was conducted over four weeks and included forty-five dental students in their final year. Students were assessed using objective structured radiographic interpretation and objective structured clinical examination assessments before (baseline) and after the e-learning modules. Student perceptions of the modules were evaluated quantitatively using an online questionnaire and qualitatively in focus group discussions. Quantitative data were analyzed using a Wilcoxon signed rank test. Qualitative data from focus group discussions were analyzed using thematic analysis. RESULTS: After the modules, students' DPR interpretation skills improved for reporting radiographic findings (all p < 0.01), anatomical landmark identification (all p < 0.05), and pathology detection (all p < 0.05). Communication skills improved for reduced jargon usage (p < 0.01). Compliance with DPR reporting increased from 63.3% before to 81.3% after the module (p = 0.03). Students indicated positive perceptions of the modules, highlighted their flexibility, and provided reinforcement. CONCLUSION: The e-learning modules with DPR and VDC significantly enhanced student skills in DPR interpretation and clinical communication. These findings support the integration of e-learning modules in dental curricula to improve diagnostic accuracy, knowledge retention, and communication skills.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".