The value of microRNA-203 as a biomarker for the prognosis of esophageal cancer
Bibliographic record
Abstract
BACKGROUND: Previous studies have reported that microRNA-203 has an effect on the prognosis of with esophageal cancer (EC). However, the conclusion is remains controversial. Therefore, this study will try to explore the effect of high expression of microRNA-203 on the prognosis of EC patients. METHODS: Eligible studies were searched from Google Scholar, Embase, PubMed, Medline, Web of Science, Cochrane Library, China National Knowledge Infrastructure, China Scientific Journal Database, Chinese BioMedical Database and Wanfang Database. Papers in English or Chinese published from their inception to November 2020 will be included without any restrictions. Stata 14.0 and Review Manager 5.3 software were used for data analysis. Hazard ratios (HRs) and its 95% confidence intervals (CIs) were used to assess the prognostic effect of microRNA-203 on overall survival (OS) and disease-free survival (DFS). Methodological quality for each eligible trial will be assessed by using the Newcastle-Ottawa Quality Assessment Scale (NOS). RESULTS: This study will provide a high-quality evidence-based medical evidence of the correlations between microRNA-203 expression and OS and DFS. CONCLUSION: The findings of this meta-analysis will show the effect of high expression of microRNA-203 on the prognosis of EC patients, and may find a new prognostic marker for EC. INPLASY REGISTRATION NUMBER: INPLASY2020110022.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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".