High linc00473 expression indicated worse cancer prognosis, based on the meta-analysis
Bibliographic record
Abstract
BACKGROUND: Growing evidence reported that linc00473 was abnormally expressed in various tumor tissues, elevated linc00473 expression manifested unsatisfactory survival outcome. Due to the inconsistent results from different research, a meta-analysis was executed to examine the link between linc00473 level and cancer prognosis. METHODS: An exhaustive search was made from relative databases, suitable publications were enrolled based on the inclusion and exclusion criteria. The overall literature quality was assessed based on the Newcastle-Ottawa Scale (NOS) score. Relevant data was extracted such as overall survival (OS), TNM stage and lymph node metastasis (LNM) and so on. To determine the stability and dependability of the aggregated results, sensitivity analysis was implemented, while Begg's analysis was applied to examine possible publication bias. RESULTS: This meta-analysis encompassed 15 publications totaling 1144 patients. Linc00473 showed elevation in most tumor tissues, with heightened linc00473 expression suggesting inferior cancer prognosis (HR: 1.850, 95% confidence interval (CI): 1.230-2.80, P = 0.003). Subgroup analysis findings revealed comparable negative prognostic implications in the respiratory system (HR: 2.23, 95%CI: 1.39-3.58, P = 0.0008), reproductive system (HR: 3.19, 95%CI: 2.00-5.11, P < 0.00001) and additional systems (HR: 2.56, 95%CI: 1.37-4.80, P = 0.003). Moreover, elevated linc00473 expression correlated with advanced TNM stage (OR: 3.78, 95%CI: 2.75-5.19, P < 0.00001), distant metastasis (OR: 4.61, 95%CI: 2.53-8.39, P < 0.00001), increased tumor size (OR: 3.10, 95%CI: 2.13-4.50, P < 0.00001) and poor histological grade (OR: 1.85, 95%CI: 1.29-2.67, P = 0.0009). Sensitivity analysis confirmed the overall results' stability and reliability, while Begg's analysis indicated no substantial publication bias existed among the examined publications. CONCLUSION: High linc00473 expression has been detected across diverse tumor tissues, high linc00473 expression implies poor cancer prognosis, linc00473 potentially functions as a prospective therapeutic target and prognostic indicator. More high-quality documents were wished to implement to support the results of this study.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.041 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".