Tobacco Consumption Induced Changes in the Healthy Oral Mucosa and its Effect on Differential Diagnosis of Oral Lesions - A Clinical In Vivo Raman Spectroscopic Study
Bibliographic record
Abstract
Objective: To investigate tobacco consumption induced changes in the in vivo Raman spectra of oral mucosa of healthy volunteers and to study its effect on the differential diagnosis of oral lesions. Materials and Methods: The clinical in vivo study involved 28 healthy volunteers and 171 patients having malignant and potentially malignant lesions of the oral cavity. Twenty of the healthy volunteers had habits of either smoking and/or of chewing tobacco while the rest did not have any tobacco consumption habits. The in vivo Raman spectra were measured using a compact and portable near-infrared Raman spectroscopic system. A probability based multi-class diagnostic algorithm, developed for supervised classification, was employed to classify the whole set of measured tissue Raman spectra into various categories. Results: It was found that the Raman spectra of healthy volunteers with tobacco consumption habits could be separated from the spectra of those without any habit of tobacco consumption with an accuracy of over 95%. Further, it was found that exclusion of the spectral data of the oral cavity of the healthy volunteers from the reference normal database considerably improved the overall classification accuracy (92.3% as against 86%) of the algorithm in separing the oral lesions from the normal oral mucosa. Conclusion: The results of the clinical study demonstrate the potential of Raman spectroscopy in screening tobacco users who are at an increased risk of developing dysplasia or malignancy. Further, the results also show that for accurate discrimination of oral lesions based on their Raman spectra, the reference normal database should exclude spectral data of tobacco using healthy subjects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".