Salivary Metabolomics for Early Detection of Oral Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Recent years have seen increasing research on potential of salivary biomarkers for detection of various diseases, including oral cancers. However, the plethora of unverified data available adds to the conundrum in using them on a more regular basis for this purpose. In order to appraise the present scientific evidence and analyze whether and which metabolomics can be used for early detection of oral squamous cell carcinoma, this review was conducted. The review followed PRISMA 2020 guidelines and was registered in PROSPERO. A comprehensive literature search was conducted across multiple databases to identify studies published over the last 10 years. Two reviewers independently performed study selection. Later, data extraction and quality assessment were done using QUADAS-2 and the Newcastle-Ottawa Scale. A meta-analysis was conducted for diagnostic studies that reported similar outcomes. Of the 19 included studies, 12 were diagnostic and 7 observational. Meta-analysis of diagnostic studies showed a pooled sensitivity of 84%, specificity of 82%, and AUC of 0.88. Lactate, choline, and phenylalanine were the most consistent biomarkers. LC-MS was the most accurate platform (AUC: 0.91). Moderate heterogeneity (I²=58-62%) reflected methodological and population differences. Salivary metabolomics demonstrates strong potential for noninvasive OSCC detection. Standardization, larger sample sizes, and validation in diverse populations are needed for clinical translation.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".