Influence of fluorescence on screening decisions for oral mucosal lesions in community dental practices
Bibliographic record
Abstract
BACKGROUND: Quality of oral screening examinations is dependent upon the experience of the clinician and can vary widely. Deciding when a patient needs to be referred is a critical and difficult decision for general practice clinicians. A device to aid in this decision would be beneficial. The objective of this study was to to examine the utility of direct fluorescence visualization (FV) by dental practitioners as an aid in decision-making during screening for cancer and other oral lesions. METHODS: Dentists were trained to use a stepwise protocol for evaluation of the oral mucosa: medical history, head, neck and oral exam, and fluorescent visualization exam. They were asked to use clinical features to categorize lesions as low (LR), intermediate (IR), or high (HR) risk and then to determine FV status of these lesions. Clinicians made the decision of which lesions to reassess in 3 weeks and based on this reassessment, to refer forward. RESULTS: Of 2404 patients screened over 11 months, 357 initially had lesions with 325 (15%) identified as LR, 16 (4.5%) IR, and 16 (4.5%) HR. Lesions assessed initially as IR and HR had a 2.7-fold increased risk of FV loss persisting to the reassessment appointment versus the LR lesions. The most predictive model for lesion persistence included both FV status and lesion risk assessment. CONCLUSION: A protocol for screening (assess risk, reassess, and refer) is recommended for the screening of abnormal intraoral lesions. Integrating FV into a process of assessing and reassessing lesions significantly improved this model.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".