MicroRNA expression as a prognostic biomarker of tongue squamous cell carcinoma (TSCC): a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Recent studies have indicated that microRNA (miRNA) expression in tumour tissues has prognostic significance in Tongue squamous cell carcinoma (TSCC) patients. This study explored the possible prognostic value of miRNAs for TSCC based on published research. METHODS: A comprehensive literature search of multiple databases was conducted according to predefined eligibility criteria. Data were extracted from the included studies by two researchers, and HR results were determined based on Kaplan‒Meier curves according to the Tierney method. The Newcastle‒Ottawa Scale (NOS) and GRADE (Grading of Recommendations Assessment, Development, and Evaluation) pro-GDT were applied to assess the quality of all studies. Publication bias was estimated by funnel plot, Egger's rank correlation test and sensitivity analysis. RESULTS: Eleven studies (891patients) were included, of which 6 reported up-regulated miRNAs and 7 mentioned down-regulated miRNAs. The pooled hazard ratio (HR) from the prognostic indicator overall survival (OS) was 1.34 (1.25-1.44), p < 0.00001, indicating a significant difference in miRNA expression between TSCC patients with better or worse prognosis. CONCLUSION: MiRNAs may have high prognostic value and could be used as prognostic biomarkers of TSCC.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".