Optimizing Oral Cancer Screening: Latent Class Analysis of Chairside Adjuncts in a High-Risk Dental Cohort
Bibliographic record
Abstract
INTRODUCTION: Oral cancer remains a major public health challenge in high-burden regions, and late diagnosis contributes to poor survival. Conventional oral examination (COE) may be limited by subjectivity and low specificity, prompting the evaluation of adjuncts, such as toluidine blue (TB) staining, autofluorescence imaging, and oral brush cytology. This study applied latent class analysis (LCA) to estimate the unbiased diagnostic performance of multiple chairside tests in a single cohort. MATERIALS AND METHODS: A cross-sectional diagnostic study was conducted on 100 adults (aged 18-70 years) with visible oral lesions. Sequential testing included COE, VELscope autofluorescence (LED Dental Inc., White Rock, British Columbia, Canada), OralCDx brush cytology (CDx Diagnostics, Suffern, NY, USA), and 1% TB staining, which were performed under standardized conditions by calibrated examiners (kappa > 0.80). Incisional biopsy was performed for lesions that were positive in ≥2 tests. LCA modeled the true disease status without universal verification; traditional metrics used histopathology as a reference. RESULTS: Histopathology confirmed dysplasia or malignancy in 43 patients (43%). COE showed high sensitivity (83.7%), but low specificity (54.4%). TB staining yielded a sensitivity of 69.8% and specificity of 86.0%, and autofluorescence showed a sensitivity of 60.5% and specificity of 89.5%. Brush cytology achieved a balanced accuracy (sensitivity 83.7%; specificity 80.7%), with the strongest correlation with histopathology (r = 0.64). LCA identified two latent classes, brush cytology and TB, demonstrating superior class discrimination. The inter-test agreement was highest between COE and TB (r = 0.61). CONCLUSIONS: No single test was found to be optimal. Brush cytology offered the best standalone accuracy, whereas COE and autofluorescence served as sensitive initial screens. A two-tier sequential strategy comprising COE/autofluorescence followed by brush cytology maximizes case detection and reduces false positives. The LCA provides robust and unbiased estimates for real-world screening. Integration into routine dental practice with structured training can improve early detection of oral dysplasia in high-risk populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".