Adjunctive Utility of Toluidine Blue in Detecting Dysplastic Cells in Oral Mucosal Lesions in Comparison with Histopathology
Bibliographic record
Abstract
Introduction: Oral cancer is one of the most common cancers globally and in Sri Lanka, which follows premalignant lesions. It is curable if it is detected early. Several adjunctive methods to diagnose premalignant lesions early are available. Among these, Toluidine blue staining method before a biopsy is currently receiving much attention. Method: This is a prospective study done by studying 103 patients presented to the Oral and Maxillofacial Surgery Unit, District General Hospital, Gampaha, Sri Lanka. The oral lesions of all the patients are categorized as benign, premalignant, and malignant by clinical examination. Toluidine Blue mouth wash is introduced to all the patients, followed by biopsy from the stained sites and the clinically decided sites in non-stained lesions. Histopathological diagnosis was obtained for all cases. The accuracy of diagnosis of premalignant, malignant, and benign cases by clinical assessment and by using Toluidine blue was assessed and compared statistically in relation to sensitivity, specificity, positive predictive and negative predictive values, and likelihood ratios (LR). Results: Toluidine blue has no added advantage over clinical examination in our setup even though it might be helpful in screening. However, it has an added value to confirm clinically benign cases as benign. Conclusion: Toluidine Blue can be used as an adjunct in screening and to confirm clinically benign cases so that those can be followed up in clinics without doing unnecessary biopsies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".