Abstract B05: Influence of fluorescence on screening decisions for oral lesions in community dental practices
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».