Distribution of cases encountered in Oral Medicine Undergraduate Clinic: A retrospective analysis
Bibliographic record
Abstract
Oral medicine (OM) is a dental specialty concerning the diagnosis and non-surgical management of oral conditions closely related to medical disorders. This study aims to evaluate the distribution of OM cases at the International Islamic University Malaysia (IIUM) undergraduate dental polyclinic and to determine its relationship with sociodemographic background via disease category. Students’ OM logbooks across four academic years were used for convenient sampling. Disease categories were based on previously published studies. Total cases were classified into 22 diagnosis codes and analysed using SPSS version 23. A total of 1917 cases were recorded at the undergraduate dental polyclinic across four academic batches, from year 2010 until 2018. OM case with highest frequency was oral ulcer (31%), followed by temporomandibular disorder (23%), and pericoronitis (10%). In contrast, the least common cases were lichen planus (0.4%), oral potentially malignant disease (0.3%), and tumour (0.2%). With regards to age group, second decade age group was reported the most across the observation period. OM cases were higher in females across most disease categories and Malays were the highest ethnicity reported. The current study identified the most common OM cases encountered in IIUM undergraduate dental polyclinic. The findings may portray the disease burden in the Kuantan population in general. Knowledge of common cases is crucial to prepare clinicians with safe and competent management required in clinical practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".