Oral Leukoplakia Microbiome Predicts the Degree of Dysplasia and is Shaped by Smoking and Tooth Loss
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine if the oral potentially malignant disorder, oral leukoplakia (OLK), exhibited microbiome changes that predict the degree of dysplasia and the risk of malignant progression. RESULTS: We examined the microbiome in 216 swabs of OLK from 177 patients. Compared to healthy controls (n = 120 swabs from 61 patients), who were less likely to smoke and had better oral health, OLK patients exhibited an increased abundance of Rothia mucilaginosa, Streptococcus parasanguinis and S. salivarius, resembling acetaldehyde generating communities described previously. Compared to the patients' healthy contralateral normal (CLN) mucosa (n = 202), which acts as a matched control for oral health parameters, OLK exhibited increased S. infantis, Leptotrichia spp., Bergeyella spp., Porphyromonas spp. and F. nucleatum. Machine learning with clinical and microbiome data could discriminate high-risk dysplasia (moderate to severe) from low-risk dysplasia (none or mild) (sensitivity 87.4%; specificity 76.5%). Follow-up swabs were recovered from 58 patients, eight of whom progressed to a higher grade of dysplasia or OSCC and these eight patients exhibited a higher abundance of Fusobacterium species at their initial presentation. CONCLUSIONS: Our study suggests that the OLK microbiome has potential to be an aid to the prediction of dysplasia grade and the risk of malignant transformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".