Salivary MicroRNAs as Potential Noninvasive Biomarkers for the Diagnosis of Nasopharyngeal Carcinoma: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Nasopharyngeal carcinoma (NPC) is the fourth-most-prevalent cancer in both Indonesia and Asia. Globally, an estimated 133,354 cases and 80,008 deaths were attributed to NPC in 2020. Early diagnosis plays a key role in managing NPC. Molecules found in bodily fluids, such as saliva, contain compounds (including microRNAs [miRNAs]) that can aid in detecting diseases like NPC. More studies on the expression, role, use, and accuracy of salivary miRNAs as potential diagnostic biomarkers of NPC are needed. OBJECTIVE: This protocol provides a framework for conducting a scoping review aimed at mapping the expression, role, use, and diagnostic accuracy of specific salivary miRNAs as potential diagnostic biomarkers of NPC. METHODS: The guidelines established by the JBI will be followed, which include defining the research questions, identifying relevant studies, and selecting studies based on titles and abstracts. The process will involve charting the data; collating, summarizing, and reporting the findings; and consultation. The synthesis will specifically examine the expression, role, use and accuracy of salivary miRNAs as potential diagnostic biomarkers of NPC. Both quantitative and qualitative analyses will be performed. RESULTS: The selection of studies based on keywords was completed in August 2024, and the study screening and review processes were finished by November 2024. The drafting of the scoping review manuscript is currently underway. The development of this scoping review protocol was supported by funding from the Centre for Higher Education Funding and the Indonesia Endowment Fund for Education. CONCLUSIONS: The findings of this study will offer a comprehensive overview of the expression, role, use, and diagnostic accuracy of salivary miRNAs as potential diagnostic biomarkers of NPC. The findings of this review are expected to serve as a basis for further research. TRIAL REGISTRATION: Open Science Framework k3prh_v1; https://osf.io/preprints/osf/k3prh_v1. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69484.
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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 | 0.106 | 0.115 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.050 | 0.011 |
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".