To Evaluate the Efficacy of Tissue Autofluorescence (Velscope) in the Visualization of Oral Premalignant and Malignant Lesions among High-Risk Population Aged 18 Years and Above in Haroli Block of Una, Himachal Pradesh
Bibliographic record
Abstract
Introduction: Visually enhanced lesion scope (Velscope) that identifies reduction in tissue fluorescence in dysplasia can prove to be effective in screening for potentially malignant lesions. The objective of the present study was to evaluate the effectiveness of device that utilizes the principles of tissue autofluorescence (Velscope) in the detection of dysplastic and/or neoplastic changes in oral mucosal lesions using biopsy and histopathology as "gold standard." Materials and Methods: Out of nine hundred fifty patients with suspicious oral mucosal lesions, 250 patients were subjected to conventional oral examination followed by Velscope examination. The autofluorescence characteristics of 250 patients were compared with the results of histopathology. Biopsies were obtained from all suspicious areas identified on examination. The sensitivity, specificity, positive and negative predictive values were calculated for Velscope examination. Results: The Velscope examination showed sensitivity and specificity values of 75.00% (95% CI: 69.63%-80.37%) and 61.39% (95% CI: 55.35%-67.42%) respectively while the positive and negative predictive values were 31.58% (95% CI: 25.82%-37.34%) and 91.18% (95% CI: 87.66%-94.69%) respectively. Conclusion: The definite diagnosis of the presence of dysplastic tissue changes in the oral lesions cannot be made alone with the Velscopic examination. The high number of false-positive results limits its efficiency as an adjunct despite its reasonable sensitivity. However, It can serve to alleviate patient anxiety regarding suspicious mucosal lesions in a general practice setting due to high negative predictive value. Also, a combined approach of Velscope examination and conventional oral examination may prove to be an effective diagnostic tool for early detection of malignant oral mucosal lesions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".