The prognostic value of long noncoding RNA activated by TGF-β in digestive system cancers
Bibliographic record
Abstract
BACKGROUND: To systematically evaluate whether the expression level of long non-coding RNA activated by transforming growth factor-β (lncRNA-ATB) is correlated with the prognosis of digestive system cancer (DSC) patients. METHODS: PubMed, Embase, Cochrane Library, Web of Science, Springerlink, Nature, and Karger databases were searched up to April 20, 2019 by 2 experienced researchers independently. The quality of studies was assessed with the Newcastle-Ottawa scale. The Review Manager 5.2 and STATA 12.0 software were used for this meta-analysis. RESULT: Eleven studies with 1227 DSC patients were included in the meta-analysis. Except for pancreatic cancer, high expression of lncRNA-ATB was associated with lymph node metastasis (risk ratio (RR) = 1.26, 95% confidence interval (CI): 1.12-1.42, P < .001), advanced clinical staging (RR = 1.44, 95%CI: 1.23-1.69, P < .001), reduced overall survival rate (OS) (hazard ratio (HR) = 2.33, 95%CI: 1.22-4.50, P = .01), and recurrence-free survival (RFS) (HR = 2.61, 95%CI: 1.46-4.65, P = .001) compared with low lncRNA-ATB expression in DSCs. CONCLUSIONS: High expression of lncRNA-ATB was significantly correlated with poor prognosis for most DSCs. The expression level of lncRNA-ATB could be a promising prognostic biomarker for DSC patients.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.003 | 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".