Oral Lesions in a Teaching Clinic: A Retrospective Study and Systematic Review
Bibliographic record
Abstract
Background/Objectives: Oral lesions can present with a wide range of clinical appearances, often making diagnosis challenging, particularly for dental students. This study aimed to identify the most common oral lesions treated at a teaching dental clinic and to compare these findings with data from a systematic review of similar clinical settings. The goal was to inform and calibrate a clinical classification system for oral pathology used in teaching environments. Methods: A retrospective analysis was conducted using electronic medical records from a university dental clinic over the past 10 years. Oral and maxillofacial pathology cases were categorized based on clinical and histopathological diagnoses. A systematic review was also performed to provide external context, with searches conducted across four electronic databases. Two independent reviewers carried out the study selection, data extraction, and quality assessment. The review adhered to the PRISMA guidelines. Results: A total of 524 patients were identified with oral lesions. The most frequently encountered clinical diagnostic category was developmental defects, while the most common histopathological diagnosis from biopsied cases was epithelial atypia. The systematic review yielded 1215 records, of which 69 were retrieved for full-text assessment, and 28 studies met the inclusion criteria. Conclusions: The findings highlight the predominance of specific oral and maxillofacial pathoses in teaching clinic settings, underscoring the importance of targeted educational strategies to improve diagnostic confidence among students. There is also a need for more consistent diagnostic grouping in oral pathology to enable better comparison across studies and support clinical and pre-clinical teaching. By integrating these insights, we propose a referenced classification framework that may improve standardization in the clinical teaching of oral lesions and enhance diagnostic calibration and teaching effectiveness in dental education.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".