Use of fluorescent light in detecting malignant and premalignant lesions in the oral cavity: a prospective, single-blind study.
Bibliographic record
Abstract
OBJECTIVE: To determine the usefulness of the VELscope in detecting malignant and premalignant oral cavity lesions. STUDY DESIGN: Prospective clinical study. SETTING: Head and neck oncology clinic at an academic tertiary care centre. SUBJECTS AND METHODS: Eighty-five patients with a history of smoking, alcohol use, and/or head and neck cancer were recruited into the study. The VELscope was used to examine patients' oral cavities after a clinical examination. Biopsies were then taken from suspicious areas. RESULTS: Of the 85 patients included in the study, 33 underwent biopsies prompted by a clinical examination, the VELscope, or both. Biopsy results that showed invasive malignancy or dysplasias were considered positive. Five positive biopsies for premalignant lesions were detected only by the VELscope and were not visible on clinical examination. On the other hand, only one positive biopsy for a premalignant lesion was detected by the clinical examination only and not seen on the VELscope. Seven positive biopsies were detected by both methods. This indicates that the diagnostic yield from a regular examination was 47% (95% CI 23-72) and that the diagnostic yield from the addition of the VELscope was an additional 31% (95% CI 11-59). Sensitivity and specificity for the VELscope were 92% and 77%, respectively. CONCLUSION: The Velscope may add sensitivity to the clinical examination and be a useful adjunct in high-risk patients.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".