Prognostic significance of long noncoding RNA HOTAIR in hepatocellular carcinoma: A protocol for systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Homeobox transcript antisense intergenic RNA (HOTAIR), a long noncoding RNA, has been reported to associate with the prognosis of patients with hepatocellular carcinoma (HCC) in several studies, however, the definite conclusion has not been obtained for conflicting results across different studies. The aim of this study is to determine the association of HOTAIR expression with overall survival, progression-free survival, and clinical features in HCC. METHODS: PubMed, Cochrane Library, and Embase will be comprehensively searched to seek the relevant studies. The studies meeting the inclusion criteria will be included into this systematic review and meta-analysis. A combination of hazard ratio and 95% confidence interval is used to estimate the impact of HOTAIR expression on the overall survival and progression-free survival in HCC. The relationship between HOTAIR expression and clinical features of HCC is evaluated using the odds ratio and 95% confidence interval. The study quality is evaluated with the "risk of bias assessment" tool in Cochrane System Assessment Manual or Newcastle-Ottawa Scale. The subgroup analysis, publication bias, and sensitivity analysis are performed. RESULTS: This study provides a strict and classic protocol for systematic review and meta-analysis to determine the prognostic significance of HOTAIR expression in HCC. The findings of this systematic review and meta-analysis may provide a novel diagnostic indicator and potential therapeutic target of HCC. ETHICS AND DISSEMINATION: This study is only a protocol for systematic review and meta-analysis, and all data used in this study is acquired through published studies. Therefore, the ethical review is not needed for this study. REGISTRATION NUMBER: INPLASY202230050.
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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 | 0.068 | 0.128 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.027 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.004 |
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".