Adjunctive screening devices for oral lesions: their use by Canadian Dental Hygienists and the need for knowledge translation
Bibliographic record
Abstract
Screening for oral cancer and other mucosal conditions is a knowledge-to-action objective that should be easy: there is supportive evidence, it is fast and non-invasive, and the oral cavity is easy to visualize. However, over 60% of oral cancers are diagnosed late, when treatment is complex and prognosis poor. Adjunctive screening devices (ASDs), e.g. toluidine blue (TB), fluorescence visualization (FV), chemiluminescence (CL) and brush biopsies, were designed to assess risk of oral lesions or aid in identification and localization of oral premalignant and malignant lesions. Little is known on how clinicians feel about using ASDs. OBJECTIVES: To evaluate use and level of comfort in using ASDs for oral cancer screening among dental hygienists. METHODS: Online email survey of a stratified random sample of nearly 3000 dental hygienists from four Canadian provinces. RESULTS: A total of 369 hygienists responded about ASDs. Ninety-three (25%) had used an ASD. Use was associated with six or more continuing education (CE) courses per year (P = 0.030), having a CE course in oral pathology within the last 3 years (P = 0.003) and having a screening protocol (P = 0.008). The most commonly used ASD is FV, which was the tool hygienists felt most comfortable using. Few used brush biopsies. Older graduates were more comfortable using TB (P = 0.014) and CL (0.033). CONCLUSION: Current evidence and education through CE appears to bolster knowledge translation efforts for hygienists to become more comfortable in the use of ASDs. ASDs with minimal supporting evidence and not specifically targeted to hygienists, such as the brush biopsies, are not well utilized.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".