Prognostic value of microRNA-21 in epithelial ovarian carcinoma
Bibliographic record
Abstract
BACKGROUD: The expression of microRNA-21 has been shown to be associated with the prognosis in patients with malignant tumors. However, its prognostic value in epithelial ovarian carcinoma (EOC) remains controversial. This meta-analysis aimed to synthesize available data to clarify the association between microRNA-21 expression levels and clinical prognosis in EOC patients. METHODS: Eligible literatures were searched from Embase, Google Scholar, PubMed, Web of Science, Medline, Cochrane Library, China Scientific Journal Database, China National Knowledge Infrastructure, Chinese BioMedical Database and Wanfang Database to identify eligible studies. Papers in English or Chinese published from their inception to November 2020 will be included. Methodological quality for each eligible trial will be assessed by using the Newcastle-Ottawa Quality Assessment Scale. Odds ratios or hazards ratios with corresponding 95% confidence intervals were pooled to estimate the prognosis value of microRNA-21 by using Stata 14.0 and Review Manager 5.3 software. RESULTS: This study will provide a high-quality evidence-based medical evidence of the correlations between microRNA-21 expression and overall survival and disease-free survival. CONCLUSION: The findings of this systematic review will show the effect of high expression of microRNA-21 on the prognosis of EOC patients. TRIAL REGISTRATION NUMBER: INPLASY2020110064.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".