Abstract B05: Influence of fluorescence on screening decisions for oral lesions in community dental practices
Bibliographic record
Abstract
Abstract Oral cancer is a global issue, with almost 300,000 new cases reported annually. While the oral cavity is cancer site that is easily examined, >40% of oral cancers are diagnosed at a late stage when prognosis is poor and treatment can be devastating. Opportunistic screening within the dental office could lead to earlier diagnosis and intervention with improved survival. Tools to aid screening are available but it is vital to validate them within the general dental office amongst clinicians with less experience than specialists in high-risk clinics. Fluorescence visualization (FV) is a tool used to assess alterations to tissue fluorescence. The goal of this study was to determine how clinicians made decisions about referral based on the risk classification of the lesion, how FV was integrated and how it affected the decision to refer. Information on FV rates in private practice and how FV affects decision making is vital to determine the feasibility of using this tool in a general practice setting. Methods: 15 dental offices participated in a 1-day workshop on oral cancer screening, including an introduction to and use of FV. Participants then screened patients (medical history, convention oral exam, fluorescent visualization exam) in-office for 11 months. Participants were asked to triage lesions by apparent risk: low, intermediate and high. Low-risk (LR) lesions were common and benign conditions including geographic tongue, candidiasis and known trauma. High-risk (HR) lesions were white or red lesions or ulcers without apparent cause and lichenoid lesions. Clinicians then made the decision on which lesions to reassess in 3 weeks based on risk assessment and clinical judgment. Lesions of concern were seen by a community facilitator or referred to an oral medicine specialist. Results: Of 2404 patients screened, 357 had lesions with 325 (15%) identified as low risk (LR) and 32 (9%) as high risk (HR). 192 of the 357 lesions were FV+ (54%), 26 FVE (7%) and 139 FV= (39%). Factors significantly associated with the presence of lesion included older age, history of smoking, and history of drinking alcohol. Lesions which were not white in colour were more apt to be FV+ (RR=5.6; 95%CI: 3.0 – 10.4) while a rough texture was associated with FV- (RR=0.47; 95%CI: 0.25-0.88). However, rough lesions were more likely to persist to the reassessment appointment (RR=3.7; 95%CI: 1.2-11.2), as did lesions assessed at the initial appointment as HR (RR=2.7; 95%CI: 1.4-5.1). The most predictive model for lesion persistence included both FV status (FV+) and lesion risk assessment (HR). 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. With education, clinicians can eliminate low risk FV+ lesions at either the initial screening appointment or at reassessment. Citation Format: Denise M. Laronde, P Michele Williams, T Greg Hislop, Catherine Poh, Samson Ng, Chris Badjik, Lewei Zhang, Calum MacAuley, Miriam Rsoin. Influence of fluorescence on screening decisions for oral lesions in community dental practices. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr B05.
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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 | 0.004 | 0.002 |
| 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 | 0.001 |
| 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".