Diagnostic Performance of Autofluorescence for Oral Lesions: A Comparison Between a Postgraduate and an Expert Clinician
Bibliographic record
Abstract
Background/Objectives: Autofluorescence (AF) is a widely used adjunctive tool in the detection of oral potentially malignant disorders (OPMDs) and malignant lesions, but its performance can be influenced by clinicians’ experiences. This study aimed to examine how AF influences diagnostic decision-making and performances of a novice clinician compared with those of an experienced examiner. Methods: A total of 80 patients with oral lesions participated in this cross-sectional study. Each underwent a standard oral examination (OE) followed by an assessment with the VELscope® System Vx (LED Medical Diagnostics Inc., Burnaby, BC, Canada), independently conducted by an expert clinician (E) and a postgraduate dentist (PD), both blinded to each other’s results. Biopsy and histopathological analysis provided the reference diagnosis. After every examination, lesions were categorized as either “Risk of Malignancy” (RM) or “No Risk of Malignancy” (NRM). Results: Based on OE, PD identified 39 RM lesions, while E 29. AF with VELscope® identified an additional 12 RM lesions for the PD and 7 for the E that were not suspected on OE alone. Combining OE with VELscope® improved sensitivity (PD: 90.9%; E: 95.4%) and negative predictive value (PD: 91.7%; E: 97.6%), while decreasing specificity (PD: 37.9%; E: 70.7%) and positive predictive value (PD: 35.7%; E: 55.3%) compared with OE alone. Conclusions: AF increases diagnostic sensitivity, particularly for less experienced clinicians, while offering moderate advantages for experts. Nevertheless, the corresponding decline in specificity emphasizes the need for cautious interpretation. AF should be incorporated as a complementary tool within structured diagnostic pathways, accompanied by adequate training, and cannot replace histopathological confirmation or clinical expertise.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".