Diagnostic salivary biomarkers in oral cancer and oral potentially malignant disorders and their relationships to risk factors – A systematic review
Bibliographic record
Abstract
INTRODUCTION: Oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMD) are a significant health burden globally. Smoking, alcohol, and betel quid are the main risk factors. Lack of screening methods has been highlighted as a significant challenge in management. Salivary biomarkers are proposed as noninvasive diagnostic tools. The aim of this systematic review was to study salivary biomarkers reported in OSCC and OPMD. Specific objectives were to select a salivary biomarker panel suitable for early detection of OSCC and OPMD and to assess relationships between salivary biomarkers and risk factors. METHODS: Electronic literature search was conducted in academic databases (Scopus, Medline, Embase and Web of Science) without any restrictions. Following calibration, two blinded reviewers screened the studies and extracted data. A risk of bias assessment was conducted using Newcastle Ottawa scale. 295 studies were included with descriptive data analysis. EXPERT OPINION: A salivary biomarker panel including Interleukin (IL) 1β, IL6, and IL8 was selected for OSCC and OPMD. Reported relationships between salivary biomarkers and risk factors are discussed and research gaps are highlighted. Future research should be directed to assess potential salivary biomarkers and their relationships to risk factors in order to understand the biomarker's role in disease initiation.
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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 | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".